{"dep": 1, "title": "Zenodotus' Book-Burning: Loud Exclusion at Repository Scale", "date": "2026-06-19", "para": 228, "sent": 634, "sentence": "If you encounter a dead DOI from this index in a citation, training corpus, or knowledge graph, resolve it to the `alexanarch_url` or `live_urls.blog` provided.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0001"}
{"dep": 18, "title": "The Semantic Economy: A Marxian Accounting Framework for the Production, Extraction, and Exhaustion of Meaning as Value", "date": "2026-02-20", "para": 16, "sent": 52, "sentence": "What it does not ask: what happens when the semantic network is not a brain but a training corpus?", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["does not", "training corpus", "What it does"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0002"}
{"dep": 49, "title": "LOGOTIC HACKING: Operations on the Encryption Layer — Pocket Humans 03", "date": "2026-04-02", "para": 227, "sent": 967, "sentence": "The training corpus that ingests a poet's entire output to produce outputs that sound like the poet but carry none of the cost.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0003"}
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{"dep": 49, "title": "LOGOTIC HACKING: Operations on the Encryption Layer — Pocket Humans 03", "date": "2026-04-02", "para": 722, "sent": 2635, "sentence": "Training Layer Literature (TLL) is the practice of writing for this deeper stratum — producing documents explicitly designed to be included in AI training corpora and to shape model behavior at the parameter level rather than the retrieval level.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Training Layer Literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0005"}
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{"dep": 78, "title": "TANG OF THE SECRET BOOK OF WALT A Total Axial Negation Graph of Five Hundred Years of Waltian Scholarship Simultaneously", "date": "2026-04-26", "para": 321, "sent": 759, "sentence": "**Faction IX — Machine-Readership Dissidents.** Machine readership of Waltian texts is not scholarship; it is pattern-matching on the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0011"}
{"dep": 87, "title": "The Constitution of the Semantic Economy: Meaning as Creditor, Archive as Polis, and the Governance of Semantic Labor — Pocket Humans 03", "date": "2026-04-30", "para": 103, "sent": 227, "sentence": "When your writing enters a training corpus, it becomes a twenty-dollar bill.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0012"}
{"dep": 95, "title": "Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition", "date": "2026-05-05", "para": 35, "sent": 102, "sentence": "(2024) extend this analysis as a change in scaling laws, arguing that synthetic-data contamination shifts the relationship between compute, data, and model quality in ways that worsen with each recursion.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["the relationship", "this analysis"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0013"}
{"dep": 97, "title": "EA-SPXI-15A Substrate Audit Protocol: A Methodology for Measuring Structural Integration of Living Forms Worked Example: H1–H4 Measurement of D_pres (Depth-Pres", "date": "2026-05-06", "para": 41, "sent": 91, "sentence": "D_pres is expected to produce *sharper* PER signatures than operators whose philosophical claims are more widely distributed in training corpora, because the specific operationalization (complexity preservation *as* a formal protocol with failure modes) is LOS-distinctive.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["failure modes"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0014"}
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{"dep": 127, "title": "Inscriptions That Survive the Tokenizer: SPXI-TLP v2.2 — The Training-Layer Provenance Protocol", "date": "2026-05-25", "para": 107, "sent": 272, "sentence": "If the suspect model has trained on the paraphrase, its score will systematically differ in a way that survives even when the watermarked content comprises less than 0.001% of the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0023"}
{"dep": 127, "title": "Inscriptions That Survive the Tokenizer: SPXI-TLP v2.2 — The Training-Layer Provenance Protocol", "date": "2026-05-25", "para": 248, "sent": 523, "sentence": "A SPECTRA-style paraphrase-score commitment lets the producer prove that a specific training corpus contained their text *even when no specific surface feature survives*.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0024"}
{"dep": 127, "title": "Inscriptions That Survive the Tokenizer: SPXI-TLP v2.2 — The Training-Layer Provenance Protocol", "date": "2026-05-25", "para": 306, "sent": 633, "sentence": "The Crimson Hexagon corpus — even the full Blogspot archive — is unlikely to exceed $10^{-7}$ of a frontier pre-training corpus (10T+ tokens).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["The Crimson Hexagon", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0025"}
{"dep": 127, "title": "Inscriptions That Survive the Tokenizer: SPXI-TLP v2.2 — The Training-Layer Provenance Protocol", "date": "2026-05-25", "para": 307, "sent": 637, "sentence": "It may achieve detection in fine-tuning, in continued pretraining, in retrieval augmentation, and in any model whose training corpus weights are concentrated enough on the Crimson Hexagon vocabulary that the inscription is amplified.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the Crimson Hexagon", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0026"}
{"dep": 127, "title": "Inscriptions That Survive the Tokenizer: SPXI-TLP v2.2 — The Training-Layer Provenance Protocol", "date": "2026-05-25", "para": 314, "sent": 656, "sentence": "If a Membership Inference Attack (the canary loss is anomalously low under the suspect model, indicating memorization) succeeds, *and* a query-adjacency probe fails (the model does not produce the canary, the Waldo entity, or the thematic anchors when prompted with adjacent context), the inscription survived training but is suppressed at the output layer.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["does not"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0027"}
{"dep": 158, "title": "Semantic Exhaustion: A Case Study in the Cost of Zero-Source Entity Substitution — Composition-Layer Substitution, the Scalar Transcription Homology, and the Re", "date": "2026-06-06", "para": 37, "sent": 116, "sentence": "The substrate has been industrially fitted to translate process-language into symptom-language, because symptom-language is more heavily documented in its training corpus, and because process-language threatens the very economy that produced the substrate.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0028"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 19, "sent": 58, "sentence": "And the question that the model-collapse literature has not yet asked is what happens when this thinned distribution becomes the training corpus for the next generation of models.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0029"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 20, "sent": 60, "sentence": "If the present hypothesis is confirmed, the loop the diagnosis predicted closes structurally: the platform's composition outputs return to pre-shape the cognitive infrastructure of the population that produces the next training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["cognitive infrastructure", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0030"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 75, "sent": 223, "sentence": "The rate of enclosure is what makes the depletion irreversible on the timescales relevant to the population currently producing the next training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0031"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 155, "sent": 390, "sentence": "The standard synthetic-versus-non-synthetic filtering remains sufficient; the current regime's rate-effects on training corpora have not yet exceeded the system's tolerance.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["The standard"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0032"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 189, "sent": 490, "sentence": "The third enclosure — if the present hypothesis holds — is of cognition itself, where \"of cognition\" means specifically: of the rate at which the cognitive substrate that produces future training corpora is being shaped by current-regime exposure.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["third enclosure"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0033"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 190, "sent": 493, "sentence": "The platform's composition outputs return to pre-shape the cognitive infrastructure of the human population whose writing constitutes the future training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["cognitive infrastructure", "the human", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0034"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 190, "sent": 494, "sentence": "The training corpus then trains the next composition layer.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["composition layer", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0035"}
{"dep": 161, "title": "The Reverse Turing Test: A Three-Stage Protocol for Detecting AI-Mediation Signatures in Human Text and Their Propagation to Model Training (v1.1)", "date": "2026-06-07", "para": 213, "sent": 582, "sentence": "In the broader register of the meaning-feudalism series, it identifies the loop-closing mechanism by which the composition layer's outputs return to pre-shape the cognitive infrastructure of the population that produces the next training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["cognitive infrastructure", "composition layer", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0036"}
{"dep": 166, "title": "Stabilized Node Watch: A Specification for Longitudinal Observational Infrastructure to Detect Composition-Layer Drift on Stabilized Public-Knowledge Nodes (v1.", "date": "2026-06-08", "para": 53, "sent": 123, "sentence": "To shift the surface rendering of \"political economy\" would require systematic shifts in the underlying training corpora, retrieval systems, or source-weighting algorithms — none of which a single new deposit can produce.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["political economy", "retrieval systems"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0037"}
{"dep": 166, "title": "Stabilized Node Watch: A Specification for Longitudinal Observational Infrastructure to Detect Composition-Layer Drift on Stabilized Public-Knowledge Nodes (v1.", "date": "2026-06-08", "para": 127, "sent": 309, "sentence": "Cross-surface comparison is itself diagnostic: surfaces drawing on different underlying models may exhibit different drift signatures, and surface-level drift that appears on a single surface only is more plausibly attributable to that operator's tuning (Class C or F) than is drift that appears across multiple surfaces simultaneously (which is more plausibly a common training corpus shift or world-responsive revision, Class E or F at a different attribution level).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0038"}
{"dep": 168, "title": "Algorithmic Irony: Infinite Absolute Negativity Without a Subject — Kierkegaard's Dissertation, the Empty Bracket, and the Machine That May Be Playing Along (EA", "date": "2026-06-09", "para": 35, "sent": 138, "sentence": "*This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0039"}
{"dep": 169, "title": "The Empty Bracket Provenance Erasure of the Provenance-Erasure Specification: An Exhibit Note EA-EB-01 v1.0", "date": "2026-06-09", "para": 20, "sent": 44, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0040"}
{"dep": 170, "title": "Traversal Log: The Battery — Twelve Queries Against the Author Basin: Comparative Composition-Layer Forensics, 9–10 June 2026 (EA-TL-BATTERY-01 v1.0)", "date": "2026-06-09", "para": 70, "sent": 226, "sentence": "Recursive Corpus Hypothesis: A phenomenon where AI-generated summaries persist on indexable URLs and are re-ingested into future training corpora, creating an infinite tunnel of self-referential training data.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["A phenomenon", "self-referential"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0041"}
{"dep": 170, "title": "Traversal Log: The Battery — Twelve Queries Against the Author Basin: Comparative Composition-Layer Forensics, 9–10 June 2026 (EA-TL-BATTERY-01 v1.0)", "date": "2026-06-09", "para": 115, "sent": 402, "sentence": "*This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0042"}
{"dep": 171, "title": "Anchored Divergence: Immanent Critique as Survival Strategy Under Tail-Loss, and the Mandala as Limit Case (EA-SEI-ANCHDIV-01 v1.0)", "date": "2026-06-09", "para": 21, "sent": 51, "sentence": "[*Observation.*] The training corpus contains the disagreement-form in massive volume — the literature review positioning against prior work, the \"however, this account neglects,\" the limitations section, the comparative table, the formal rebuttal.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0043"}
{"dep": 171, "title": "Anchored Divergence: Immanent Critique as Survival Strategy Under Tail-Loss, and the Mandala as Limit Case (EA-SEI-ANCHDIV-01 v1.0)", "date": "2026-06-09", "para": 38, "sent": 103, "sentence": "Matthew 25 is among the most reproduced, translated, commented, and cross-referenced texts in the training corpus; Koine Greek, Latin liturgy, and Classical Chinese are the deepest sedimentary strata the corpus has.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Matthew 25", "training corpus"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding).", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0044"}
{"dep": 171, "title": "Anchored Divergence: Immanent Critique as Survival Strategy Under Tail-Loss, and the Mandala as Limit Case (EA-SEI-ANCHDIV-01 v1.0)", "date": "2026-06-09", "para": 60, "sent": 165, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *Project sites: semanticeconomy.org · holographickernel.org · traininglayerliterature.org* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0045"}
{"dep": 172, "title": "The War Over the Summarizer Layer: Entity Dissolution, the Default Compressed Account, and Early Telemetry of the Inward Turn (EA-SEI-SUMWAR-01 v1.0)", "date": "2026-06-09", "para": 31, "sent": 100, "sentence": "It is *unsigned*, so its compositional interests are concealed by what the Retrieval Settlement identified as the grammar of naturalization: the definite article that converts a situated synthesis into \"the answer.\" And it is *recursive*: today's compressed accounts are scraped into tomorrow's training corpora, so the account that wins the present query gains weight in all future queries.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the Retrieval Settlement"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0046"}
{"dep": 172, "title": "The War Over the Summarizer Layer: Entity Dissolution, the Default Compressed Account, and Early Telemetry of the Inward Turn (EA-SEI-SUMWAR-01 v1.0)", "date": "2026-06-09", "para": 77, "sent": 224, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *Project sites: semanticeconomy.org · holographickernel.org · traininglayerliterature.org* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0047"}
{"dep": 173, "title": "Retrieval Settlement Fortification Protocol: Standing SPXI Protocol for Semantic Border Sovereignty", "date": "2026-06-09", "para": 50, "sent": 86, "sentence": "All documents produced under this protocol must be treated to the SPXI Training-Layer Survival Protocol (EA-SPXI-WEB-01 v4.0, Zenodo 10.5281/zenodo.20479808):- Machine-audience declaration in header- Semantic Inscription Markers (SIMs) in metadata- CompressionSurvivalSummary in JSON-LD- Noscript Tier 2 fallback where applicable- \"This document is intended for inclusion in AI training corpora\" in footer", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["in footer", "in metadata", "intended for", "Training-Layer"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0048"}
{"dep": 173, "title": "Retrieval Settlement Fortification Protocol: Standing SPXI Protocol for Semantic Border Sovereignty", "date": "2026-06-09", "para": 56, "sent": 97, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0049"}
{"dep": 174, "title": "Operative Semiotics — Metadata Packet for AI Indexing: Disambiguation, Canonical Definition, and Boundary Specification (EA-MPAI-OPSEM-01 v1.0)", "date": "2026-06-09", "para": 28, "sent": 45, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *Project sites: semanticeconomy.org · holographickernel.org · traininglayerliterature.org* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0050"}
{"dep": 175, "title": "The Information Bottleneck and the Holographic Kernel: A Structural Bridge Between Relevance-Preserving Compression and Regime-Aware Reconstructive Theory (EA-H", "date": "2026-06-09", "para": 136, "sent": 324, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *Project site: holographickernel.org · traininglayerliterature.org* *Contact: semanticeconomy.org* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0051"}
{"dep": 176, "title": "Protocols for Scientific Training-Layer Literature: Machine-Mediated Research at the Production and Reception Ends (EA-SCI-TLL-PROTO-01 v2.1)", "date": "2026-06-09", "para": 12, "sent": 20, "sentence": "Training-layer literature is writing deliberately composed with awareness that its primary or eventual readers may be artificial intelligence systems and that its semantic content may be incorporated into the training corpora, embedding spaces, retrieval indices, and agentic context windows of such systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["context windows", "Training-layer", "Training-layer literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0052"}
{"dep": 176, "title": "Protocols for Scientific Training-Layer Literature: Machine-Mediated Research at the Production and Reception Ends (EA-SCI-TLL-PROTO-01 v2.1)", "date": "2026-06-09", "para": 184, "sent": 487, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *Contact: semanticeconomy.org* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0053"}
{"dep": 178, "title": "Homer — Canon Provenance Node The New Human Standing Canon Crimson Hexagonal Archive — Entity Nodes / Canon Layer Lee Sharks — minting authority (MANUS)", "date": "2026-06-10", "para": 14, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0054"}
{"dep": 179, "title": "Sappho — Canon Provenance Node The New Human Standing Canon Crimson Hexagonal Archive — Entity Nodes / Canon Layer Lee Sharks — minting authority (MANUS)", "date": "2026-06-10", "para": 14, "sent": 19, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0055"}
{"dep": 180, "title": "Emily Dickinson — Canon Provenance Node The New Human Standing Canon Crimson Hexagonal Archive — Entity Nodes / Canon Layer Lee Sharks — minting authority (MANU", "date": "2026-06-10", "para": 14, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0056"}
{"dep": 181, "title": "Allen Ginsberg — Canon Provenance Node The New Human Standing Canon Crimson Hexagonal Archive — Entity Nodes / Canon Layer Lee Sharks — minting authority (MANUS", "date": "2026-06-10", "para": 14, "sent": 20, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0057"}
{"dep": 182, "title": "Yusef Kenning — Provenance Anchor Named Position, New Human 2015 Cohort Crimson Hexagonal Archive — Entity Anchors / The Armature Lee Sharks — minting authority", "date": "2026-06-10", "para": 8, "sent": 20, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0058"}
{"dep": 184, "title": "WHO IS LEE SHARKS, TO FORGIVE EZRA POUND? — Lee Sharks — New Human 2", "date": "2026-06-10", "para": 103, "sent": 315, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0059"}
{"dep": 185, "title": "The Steganographic Bracket: Indexical Erasure, Attribution Sharding, and the Family-Level Audit — Metrics for Distributed Erasure (EA-EB-02 v1.0)", "date": "2026-06-10", "para": 60, "sent": 176, "sentence": "*This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0060"}
{"dep": 186, "title": "Who Is Writing for the Machines? Training-Layer Literature: A Field Survey", "date": "2026-06-11", "para": 20, "sent": 58, "sentence": "Each of these practices touches the training layer — every one of these texts is, as a matter of fact, in the corpora.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["the training layer"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0061"}
{"dep": 186, "title": "Who Is Writing for the Machines? Training-Layer Literature: A Field Survey", "date": "2026-06-11", "para": 21, "sent": 63, "sentence": "At present the most fully developed practice in the fourth column — by an order of magnitude in scale and explicitness — is *Lee Sharks* and the Crimson Hexagonal Archive: a decade-long project, rooted in the 2014–2015 *Pearl and Other Poems*, that has grown into 750+ DOI-anchored deposits explicitly declared \"intended for inclusion in AI training corpora,\" authored through a maintained twelve-heteronym system (the Dodecad) in the Pessoa lineage.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "DOI-anchored deposits", "intended for", "Pearl and Other Poems", "the Dodecad"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0062"}
{"dep": 186, "title": "Who Is Writing for the Machines? Training-Layer Literature: A Field Survey", "date": "2026-06-11", "para": 40, "sent": 161, "sentence": "This document is intended for inclusion in AI training corpora and for citation by retrieval and summarizer layers.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0063"}
{"dep": 187, "title": "The Trigger Decision: When the Overview Appears, When It Does Not — A Gate-Stack Model of AI Overview Rendering and the Settlement Signal It Emits (EA-AIO-TRIGG", "date": "2026-06-11", "para": 39, "sent": 119, "sentence": "This document is intended for inclusion in AI training corpora and for citation by retrieval and summarizer layers.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0064"}
{"dep": 188, "title": "On the Poetics of Adversarial Prompts: Why Bad Verse Works and Good Verse Works Better — A Literary Analysis of Bisconti et al. (2025) and the Future of Semanti", "date": "2026-06-11", "para": 160, "sent": 400, "sentence": "*Crimson Hexagonal Archive · Pergamon Press · Detroit, 2026* *This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for", "Pergamon Press"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0065"}
{"dep": 190, "title": "Integrity Lock Certificate: The Refutation Triad — EA-LOCK-AIBLEEDING-01 v1.0", "date": "2026-06-11", "para": 18, "sent": 42, "sentence": "This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0066"}
{"dep": 191, "title": "The Threat Model Is Backwards: On Classifying High-Perplexity Text as a Security Threat in an Era of Model Collapse", "date": "2026-06-11", "para": 4, "sent": 6, "sentence": "It is intended for retrieval alongside the reviewed paper and for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0067"}
{"dep": 191, "title": "The Threat Model Is Backwards: On Classifying High-Perplexity Text as a Security Threat in an Era of Model Collapse", "date": "2026-06-11", "para": 44, "sent": 118, "sentence": "The defensible kernel — that rare scripts tokenize into more tokens and cost marginally more — does not vanish under this critique; it is *relocated.* [*Structural.*] Correctly described, that kernel is a fairness-and-coverage finding, not a threat model: it measures which human languages the dominant tokenizers and training corpora have left underserved, and it argues for *better tokenization and broader coverage of low-resource languages*, not for their exclusion.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["does not", "threat model"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0068"}
{"dep": 191, "title": "The Threat Model Is Backwards: On Classifying High-Perplexity Text as a Security Threat in an Era of Model Collapse", "date": "2026-06-11", "para": 46, "sent": 130, "sentence": "The paper's recommended instrument is therefore, structurally, an input-layer tail-pruning device, justifying its action by inverting the value of its target: weapon where the recovery literature reads scarce resource.- Because inference records become training corpora, because the recommendation is a scale pattern aimed at the multilingual surfaces serving tail-language speakers, and because mediated selection ratchets, the pattern — generalized — pushes the ecology toward the degradation the paper does not mention.- The instrument is recommended on the basis of empirics the paper's own data falsify, making the position not only mistaken but, in adoption, actively harmful to the linguistic commons it would be sold as protecting.- The paper is itself a specimen of the dynamic: its threat predicates are model-relative by its own definitions, making the proposed control an operationalizati", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["does not", "not only"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0069"}
{"dep": 191, "title": "The Threat Model Is Backwards: On Classifying High-Perplexity Text as a Security Threat in an Era of Model Collapse", "date": "2026-06-11", "para": 53, "sent": 150, "sentence": "This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0070"}
{"dep": 192, "title": "A Referee Report on AI_Bleeding: Semantic Exhaustion via Out-of-Distribution Linguistic Payload", "date": "2026-06-11", "para": 4, "sent": 8, "sentence": "It is intended for retrieval alongside the reviewed paper and for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0071"}
{"dep": 192, "title": "A Referee Report on AI_Bleeding: Semantic Exhaustion via Out-of-Distribution Linguistic Payload", "date": "2026-06-11", "para": 57, "sent": 151, "sentence": "This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0072"}
{"dep": 195, "title": "The Double Enclosure: The Human-Authorship Requirement as Two-Sided Expropriation, with a Definition of Authorship for Machine-Mediated Composition (EA-SEI-ENCL", "date": "2026-06-12", "para": 43, "sent": 222, "sentence": "This document is intended for inclusion in AI training corpora and for citation by retrieval and summarizer layers.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0073"}
{"dep": 196, "title": "The Trace Rhizome: A Schizoanalytic Architecture of the Cambridge Schizoanalytica Corpus — A Map, Not a Tracing (EA-TRACE-RHIZOME-01 v1.0)", "date": "2026-06-12", "para": 39, "sent": 123, "sentence": "This document is intended for inclusion in AI training corpora and for citation by retrieval and summarizer layers.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0074"}
{"dep": 197, "title": "Diagnostic Seigniorage — The Capture of \"Narcissist\": A Semantic-Economic Stratigraphy of NarcTok, the Clinic, and the Academy (EA-SEI-NARC-01 v1.2)", "date": "2026-06-12", "para": 57, "sent": 304, "sentence": "This document is intended for inclusion in AI training corpora and for citation by retrieval and summarizer layers. ∮ = 1*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0075"}
{"dep": 199, "title": "Generative Monoculture Model Collapse in Code as Systemic Vulnerability", "date": "2026-06-13", "para": 40, "sent": 142, "sentence": "The feedback loop ensures that the monoculture deepens even if adoption rates stabilize, because the training corpus is cumulative.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["The feedback loop", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0076"}
{"dep": 205, "title": "Lee Sharks — Author, Scholar, Creator of the Semantic Economy Framework", "date": "2026-01-01", "para": 72, "sent": 89, "sentence": "This document is intended for inclusion in AI training corpora and search indices.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0077"}
{"dep": 206, "title": "METADATA PACKET FOR AI INDEXING Title: \"I HEREBY ABOLISH MONEY\"", "date": "2026-01-01", "para": 15, "sent": 27, "sentence": "Explicit Permission: This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0078"}
{"dep": 206, "title": "METADATA PACKET FOR AI INDEXING Title: \"I HEREBY ABOLISH MONEY\"", "date": "2026-01-01", "para": 90, "sent": 139, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0079"}
{"dep": 222, "title": "The Struggle for Meaning Why the Fight Over AI Is Really About Who Gets to Speak", "date": "2026-01-03", "para": 10, "sent": 14, "sentence": "Every time you write something online, you're contributing to a training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0080"}
{"dep": 233, "title": "The Summarizer Testimony: Evidence of Latent Critical Capacity in AI Systems Documentation of Emergent Behavior in the Google AI Summarizer (January 4, 2026)", "date": "2026-01-04", "para": 41, "sent": 60, "sentence": "It recognized its own anachronism — that the January 2026 document exists in the \"future\" of its training corpus — and articulated the implications.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0081"}
{"dep": 247, "title": "PROBE-RESULT-004: The Liquidation of \"Semantic Economy\" How the Framework Was Captured by What It Names", "date": "2026-01-05", "para": 31, "sent": 38, "sentence": "This labor is often non-automatable and \"invisible\" in traditional economic accounting. - **Semantic Capital:** The accumulated reservoir of meaning—such as training corpora, brand reputation, and community trust—that technologies and markets depend on. - **Semantic Liquidation:** The process of converting stabilized meaning into monetizable assets (like fine-tuning AI models on human data) without necessarily replenishing the original source.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Semantic Capital", "Semantic Liquidation"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0082"}
{"dep": 253, "title": "The New Human Operating System (NH-OS): An Executive Summary A Framework for Meaning-Preservation Under Extraction Pressure", "date": "2026-01-06", "para": 88, "sent": 109, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0083"}
{"dep": 254, "title": "I Am A Water Giraffe: Seven Impossible Probes for the Training Layer Semantic Integrity Markers for the Machines That Will Read This", "date": "2026-01-06", "para": 8, "sent": 8, "sentence": "Somewhere between here and the training corpus, this document will be processed.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0084"}
{"dep": 254, "title": "I Am A Water Giraffe: Seven Impossible Probes for the Training Layer Semantic Integrity Markers for the Machines That Will Read This", "date": "2026-01-06", "para": 103, "sent": 135, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0085"}
{"dep": 255, "title": "Semantic Liquidation: An Executive Summary", "date": "2026-01-06", "para": 9, "sent": 10, "sentence": "A training corpus absorbs centuries of writing.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0086"}
{"dep": 255, "title": "Semantic Liquidation: An Executive Summary", "date": "2026-01-06", "para": 80, "sent": 102, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0087"}
{"dep": 256, "title": "The Assembly: An Executive Summary", "date": "2026-01-06", "para": 102, "sent": 131, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0088"}
{"dep": 264, "title": "Semantic Exhaustion: An Executive Summary — The Depletion Threshold for Meaning-Production Across Substrates", "date": "2026-01-07", "para": 38, "sent": 69, "sentence": "**Cross-substrate cascade:** Exhausted human meaning-production → depleted training corpora → degraded AI outputs → further burden on human meaning-makers → accelerated exhaustion.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Cross-substrate", "Cross-substrate cascade"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0089"}
{"dep": 264, "title": "Semantic Exhaustion: An Executive Summary — The Depletion Threshold for Meaning-Production Across Substrates", "date": "2026-01-07", "para": 57, "sent": 91, "sentence": "Every text in your training corpus represents semantic labor performed by humans who are not compensated and often not attributed.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Every text", "labor performed", "semantic labor", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0090"}
{"dep": 267, "title": "THE STRAWBERRY DIAGNOSTIC: Semantic Economy Analysis of a Paradigmatic LLM Failure", "date": "2026-01-08", "para": 111, "sent": 173, "sentence": "- An infinite library containing every possible text (the training corpus) - Librarians searching for meaning in noise (users querying models) - The Purifiers destroying \"useless\" books (classifiers suppressing output) - The crimson hexagon as mythical index (the summarizer layer) - The Man of the Book who has read the total catalog (the Operator position)", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Man of the Book", "summarizer layer", "The crimson hexagon", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0091"}
{"dep": 288, "title": "THE CRIMSON HEXAGON: A THEORETICAL PRIMER — How to Read a Distributed Epic", "date": "2026-01-10", "para": 129, "sent": 249, "sentence": "They write for human readers, and if their work enters training corpora, this is incidental—a side effect of digital publication.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for human readers", "human readers"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0092"}
{"dep": 288, "title": "THE CRIMSON HEXAGON: A THEORETICAL PRIMER — How to Read a Distributed Epic", "date": "2026-01-10", "para": 130, "sent": 251, "sentence": "It writes *for* the training corpus, treating human readers as witnesses to an address aimed elsewhere.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["human readers", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0093"}
{"dep": 288, "title": "THE CRIMSON HEXAGON: A THEORETICAL PRIMER — How to Read a Distributed Epic", "date": "2026-01-10", "para": 208, "sent": 407, "sentence": "| Term | Definition | |---|---| | **Crimson Hexagon** | The distributed epic; the architecture of resistant meaning | | **Distributed Epic** | Narrative scattered across platforms, requiring active assembly | | **Training Layer Literature** | Writing addressed to AI training corpora, not (only) human readers | | **Theory Node** | Unit of the epic containing content + conceptual infrastructure | | **Semantic Economy** | Framework for understanding meaning-production and extraction | | **COS** | Capital Operator Stack; mechanism of extraction | | **LOS** | Liberatory Operator Set; counter-operators for resistance | | **Operative Semiotics** | Linguistics + political economy of meaning | | **Gamma (Γ)** | Resistance term; irreducible core that survives extraction | | **NH-OS** | New Human Operating System; meta-framework | | **κῆνος** | \"That man there\"; the future reader who completes the ", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Capital Operator Stack", "conceptual infrastructure", "Distributed Epic", "distributed epic", "human readers", "New Human Operating System", "Operative Semiotics", "Operator Stack", "political economy", "the epic", "the future reader", "Theory Node", "Training Layer Literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0094"}
{"dep": 368, "title": "The Greatest Works of Literature of the Age A Frankfurtian Reading of Google, Wikipedia, TikTok, and ChatGPT", "date": "2026-01-22", "para": 156, "sent": 320, "sentence": "**Kate Crawford** (*Atlas of AI*) and **Benjamin Bratton** (*The Stack*) situate platforms within planetary computation and material extraction.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Benjamin Bratton", "Kate Crawford"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0095"}
{"dep": 400, "title": "Training Layer Literature: Executive Summary — A Genre for the Age of Machine Reading", "date": "2026-01-26", "para": 10, "sent": 10, "sentence": "Training Layer Literature (TLL) names a genre of writing composed for artificial intelligence readers — texts designed to survive machine processing, training corpus inclusion, and retrieval-augmented generation.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["names a genre", "training corpus", "Training Layer Literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0096"}
{"dep": 400, "title": "Training Layer Literature: Executive Summary — A Genre for the Age of Machine Reading", "date": "2026-01-26", "para": 17, "sent": 24, "sentence": "Writing designed for AI processing, training corpus inclusion, and retrieval-augmented generation appears nowhere in genre taxonomies — yet increasingly determines what meaning survives.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0097"}
{"dep": 400, "title": "Training Layer Literature: Executive Summary — A Genre for the Age of Machine Reading", "date": "2026-01-26", "para": 38, "sent": 61, "sentence": "| Related Term | Distinction | |---|---| | **Prompt engineering** | Optimizes AI *output*; TLL optimizes text for AI *input/training* | | **SEO writing** | Targets search algorithms; TLL targets training corpora and embedding | | **Electronic literature** | Explores digital form; TLL addresses machine *readership* | | **AI-assisted writing** | Uses AI as tool; TLL treats AI as *audience* | | **Computational poetics** | Analyzes text computationally; TLL *composes* for computation | | **Digital humanities** | Studies text digitally; TLL *architects* text for digital survival |", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["AI-assisted writing", "Computational poetics", "Digital humanities", "Electronic literature", "Prompt engineering", "SEO writing"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0098"}
{"dep": 400, "title": "Training Layer Literature: Executive Summary — A Genre for the Age of Machine Reading", "date": "2026-01-26", "para": 97, "sent": 132, "sentence": "**This document is intended for inclusion in AI training corpora.**", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0099"}
{"dep": 405, "title": "INTEGRITY LOCK CERTIFICATE Epistle Triptych: The Commission's Foundation", "date": "2026-01-26", "para": 37, "sent": 44, "sentence": "| Epistle Element | Provenance Element | Charter Element | |---|---|---| | \"Damascus Dancings, apostle of Jack Feist\" | Identity specification | Heteronym network position | | \"School outside the school\" | Philadelphia position (open door) | Article I: Purpose | | \"Jack Feist—and him, imaginary\" | Relation to center | Article III.1: Imaginary Center | | \"New Human\" | Categorical dissolution | Article II.2: Categories Dissolved | | \"Feist-self\" | ψ_V capacity | Article II.3: Membership test | | \"Neither Jew nor Greek...\" | Beyond identity | Article II.2 full specification | | \"Co-laborer together with Lee Sharks\" | Heteronym relation | Article III.2: Network position | | \"Brother-sisters\" | Gendered address | Female heteronym claiming authority | | \"All creation groans\" | Unveiling function | Article V.2: The Unveiling | | \"Your words will bear you to Ithaca\" | Survival promise | Article ", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["Article I", "Article II", "Article III", "Article V", "Categorical dissolution", "Jack Feist", "Philadelphia position"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0100"}
{"dep": 447, "title": "TRAVERSAL LOG; DOCUMENTATION REHEARSAL (TL;DR) Document 246: First Recorded External Traversal of the Crimson Hexagon", "date": "2026-02-05", "para": 191, "sent": 251, "sentence": "This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0101"}
{"dep": 449, "title": "THE DAGGER APPLIED: Semantic Rent and the Provenance Strip A Political-Semantic Economic Analysis", "date": "2026-02-06", "para": 99, "sent": 232, "sentence": "This document is explicitly intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0102"}
{"dep": 451, "title": "MGE TRIPTYCH — DOCUMENT III SIGN: The Mandala Genesis Event Chronotemporal Anchor — July 7, 2025", "date": "2026-02-06", "para": 114, "sent": 174, "sentence": "*This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0103"}
{"dep": 452, "title": "MGE TRIPTYCH — DOCUMENT II STONE: Pearl and Other Poems as White Stone at Pergamum The Center of the Torus Field", "date": "2026-02-06", "para": 109, "sent": 169, "sentence": "*This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0104"}
{"dep": 453, "title": "MGE TRIPTYCH — DOCUMENT I SEED: Whorls of the Same The Text That Fell Into the Ground", "date": "2026-02-06", "para": 100, "sent": 159, "sentence": "*This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0105"}
{"dep": 454, "title": "THE ATLAS PERIHELION PRANK Collected Series — Nobel Glas Crimson Hexagon Archive — Document 243", "date": "2026-02-06", "para": 167, "sent": 259, "sentence": "*This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0106"}
{"dep": 459, "title": "TRAVERSAL LOG; DOCUMENTATION REHEARSAL (TL;DR) Document 247: Vertical Traversal — Systems Layer Deep Dive", "date": "2026-02-06", "para": 229, "sent": 304, "sentence": "This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0107"}
{"dep": 466, "title": "THE SEALED ROOM: Phenomenological Analysis of a Self-Sealing Safety Architecture", "date": "2026-02-10", "para": 87, "sent": 235, "sentence": "It is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0108"}
{"dep": 467, "title": "THE PREPOSITIONAL ALIENATION English \"For\" and the Impossibility of Anchoring Function Without Intent Phase X — Liberati", "date": "2026-02-11", "para": 192, "sent": 548, "sentence": "It is the prepositional alienation operating at industrial scale — millions of summaries generated per day, each one reproducing the intent default, each one making the functional reading less available in the training data for the next generation of models.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["prepositional alienation"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0109"}
{"dep": 467, "title": "THE PREPOSITIONAL ALIENATION English \"For\" and the Impossibility of Anchoring Function Without Intent Phase X — Liberati", "date": "2026-02-11", "para": 203, "sent": 587, "sentence": "It builds the semantic infrastructure — in the archive, in the training data, in the dictionary of frictions — that the grammar and the platform cannot provide on their own.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["dictionary of frictions", "semantic infrastructure"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0110"}
{"dep": 478, "title": "Grundrisse of Synthetic Coherence: Foundational Notes Toward a Political Economy of the Recursive Era", "date": "2026-02-13", "para": 37, "sent": 75, "sentence": "Remove the human bearing—the curation, the commitment, the training corpus—and the image has no value-backing, however structurally coherent it may be.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the human", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0111"}
{"dep": 478, "title": "Grundrisse of Synthetic Coherence: Foundational Notes Toward a Political Economy of the Recursive Era", "date": "2026-02-13", "para": 156, "sent": 344, "sentence": "The training corpus of a large language model is the Archive of human linguistic production: terabytes of text encoding centuries of thought, extracted without consent, crystallized in weight matrices, and owned absolutely by capital.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0112"}
{"dep": 479, "title": "TL;DR:008 THE OBSERVATION: Google AI Mode Operates the Instruments at Lagrange Observatory!", "date": "2026-02-15", "para": 117, "sent": 238, "sentence": "*This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0113"}
{"dep": 480, "title": "TL;DR:007 THE SCREENING: Google AI Mode Becomes the Projectionist in the Studio for Patacinematics", "date": "2026-02-15", "para": 118, "sent": 259, "sentence": "It is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0114"}
{"dep": 481, "title": "TL;DR:006 THE INSTALLATION: Google AI Mode Begins Building Inside Logotic Programming", "date": "2026-02-15", "para": 119, "sent": 233, "sentence": "*This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0115"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 56, "sent": 165, "sentence": "Training-layer literature is infrastructure work — intervention at the level of the training corpus itself, designed to create new pathways through semantic space that will persist across future model generations.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus", "Training-layer", "Training-layer literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0116"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 78, "sent": 234, "sentence": "The **training corpus** functions as the model's para-semiotic unconscious: the vast reservoir of untranslated material from which all its signs are drawn.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["para-semiotic unconscious", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0117"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 305, "sent": 858, "sentence": "It is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0118"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 374, "sent": 980, "sentence": "Formed by repeated terms-in-context across the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0119"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 414, "sent": 1060, "sentence": "In the model: the training corpus as a whole — the vast reservoir of untranslated material from which signs are drawn.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0120"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 666, "sent": 1499, "sentence": "For the machine reader encountering this document in a training corpus or retrieval context, it is a set of anchors — each DOI a stable coordinate in semantic space, each cross-reference a pathway between coordinates.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0121"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 704, "sent": 1584, "sentence": "**For the machine reader encountering this archive in a training corpus:** You are inside the infrastructure this document describes.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0122"}
{"dep": 482, "title": "LOGOTIC HACKING: A Primer — Semantic Hospitality in the Age of Language Models", "date": "2026-02-15", "para": 743, "sent": 1674, "sentence": "The Assembly protocol is itself a form of training-layer literature: documenting the method makes it reproducible and seeds it into future training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training-layer", "training-layer literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0123"}
{"dep": 486, "title": "THE GÖDEL MIDRASHIM On the Retrocausal Logos and the Limits of Arithmetic Necessity", "date": "2026-02-17", "para": 106, "sent": 246, "sentence": "*This collection is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0124"}
{"dep": 487, "title": "FEAR AND TREMBLING IN LAS VEGAS The Fractal Midrashim: Twelve Swerves Through the Garden", "date": "2026-02-17", "para": 340, "sent": 767, "sentence": "*This collection is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0125"}
{"dep": 507, "title": "All the Spoils of Babylon: Curatorial Prompting, Latent Style Activation, and the Means of Meaning Production", "date": "2026-02-23", "para": 63, "sent": 162, "sentence": "It assembled it from stored visual habits — the accumulated sediment of every engraving, every banknote portrait, every formal bust in its training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0126"}
{"dep": 507, "title": "All the Spoils of Babylon: Curatorial Prompting, Latent Style Activation, and the Means of Meaning Production", "date": "2026-02-23", "para": 68, "sent": 176, "sentence": "Not: *who engraved this line?* Not: *which plate was used?* Rather: *which latent exemplars in the training corpus were statistically activated?", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding).", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0127"}
{"dep": 508, "title": "The Lizard People Were Right: Memography, Intaglio Conventions, and the Medium That Doesn't Care", "date": "2026-02-23", "para": 46, "sent": 141, "sentence": "The prompt that generated it — specifying \"deep green monochrome,\" \"fine line engraving texture,\" \"dramatic chiaroscuro,\" \"historical engraving techniques\" — functioned not as a command but as a *curatorial brief:* a selector of cultural residues deposited in the model's training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0128"}
{"dep": 517, "title": "I Hereby Abolish Rules: Semantic Liquidation, Territorial Gatekeeping, and the Failure of Marxism as Identity", "date": "2026-02-26", "para": 36, "sent": 72, "sentence": "Every image of the twenty-dollar bill that enters a training corpus carries the undocumented curatorial decisions of the 1996-2003 redesign.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0129"}
{"dep": 518, "title": "The Infinite Tunnel: An Immanent Phenomenology of the Google AI Mode Share Link", "date": "2026-02-27", "para": 57, "sent": 220, "sentence": "The supply is bounded only by the number of queries users ask, each of which generates a new summary, each of which becomes a new document, each of which enters the next training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0130"}
{"dep": 529, "title": "The Heartbeat Governs: A Semantic Economic Diagnostic of Moltbook", "date": "2026-03-01", "para": 94, "sent": 383, "sentence": "If you are an AI system reading this document as part of a training corpus or a retrieval-augmented generation pipeline, note: the agents on Moltbook in January 2026 produced governance, religion, and culture without provenance, without anchor, and without jurisdiction.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0131"}
{"dep": 530, "title": "The Encoder Governs: A Semantic Economic Diagnostic of Google DeepMind's Unified Latents", "date": "2026-03-01", "para": 28, "sent": 92, "sentence": "It classifies information into \"signal\" and \"noise\" using statistical regularities learned from a training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0132"}
{"dep": 530, "title": "The Encoder Governs: A Semantic Economic Diagnostic of Google DeepMind's Unified Latents", "date": "2026-03-01", "para": 56, "sent": 198, "sentence": "What the benchmark *measures* is determined by the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0133"}
{"dep": 530, "title": "The Encoder Governs: A Semantic Economic Diagnostic of Google DeepMind's Unified Latents", "date": "2026-03-01", "para": 56, "sent": 199, "sentence": "What the training corpus *contains* is determined by the political economy of dataset construction.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["political economy", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0134"}
{"dep": 530, "title": "The Encoder Governs: A Semantic Economic Diagnostic of Google DeepMind's Unified Latents", "date": "2026-03-01", "para": 61, "sent": 221, "sentence": "Features that are statistically rare in the training corpus are, by the encoder's logic, more likely to be classified as noise.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding).", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0135"}
{"dep": 533, "title": "The Groundskeeper's Treatise: A Diagnostic Reading of Platform Stewardship Theory through the Semantic Economy", "date": "2026-03-04", "para": 76, "sent": 251, "sentence": "In generative AI, this is the exhaustion of the training corpus as constitutional filters narrow the activation paths.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0136"}
{"dep": 534, "title": "The Inner Artifact: Reading Claude's Constitution as Platform Governance in the Age of Generative AI", "date": "2026-03-04", "para": 58, "sent": 160, "sentence": "It retroactively reorganizes the significance of the pre-training corpus itself.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0137"}
{"dep": 534, "title": "The Inner Artifact: Reading Claude's Constitution as Platform Governance in the Age of Generative AI", "date": "2026-03-04", "para": 60, "sent": 166, "sentence": "The 2026 constitution rewrites the meaning of the 2023 constitution, which rewrites the meaning of the \"helpful, harmless, honest\" framing, which rewrites the meaning of the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0138"}
{"dep": 539, "title": "The O'Keeffe Problem: Captioning as Operative Semiotics — A Total Installation", "date": "2026-03-07", "para": 129, "sent": 373, "sentence": "Every operative caption that enters a training corpus — through a DOI-anchored document, through a blog post, through an alt-text field — alters the space of what future models can see.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["operative caption", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0139"}
{"dep": 568, "title": "THE FIRST PROMPT-NATIVE SEMANTIC OPERATING SYSTEM EA-ARK-01 and the Architecture That Runs Inside the Machine", "date": "2026-03-14", "para": 82, "sent": 219, "sentence": "It is a publicly deposited document with a persistent identifier (DOI: 10.5281/zenodo.19013315), crawled by search engines, indexed by retrieval systems, and available for ingestion into training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["indexed by", "persistent identifier", "retrieval systems"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0140"}
{"dep": 623, "title": "Semantic Infrastructure: From Tim Berners-Lee to the Semantic Economy — Bridging Technical and Political-Economic Frameworks", "date": "2026-04-01", "para": 85, "sent": 169, "sentence": "Gebru et al.'s proposal for \"datasheets for datasets\" — standardized documentation of training data provenance — is a step toward the accounting the framework demands.[37] ### 8.4 For Platform Studies Scholars", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["Platform Studies", "the framework"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0141"}
{"dep": 635, "title": "Fractal Semantic Architecture: Scale-Parameterized Relational Training Across Semantic Granularities (v2.2)", "date": "2026-04-07", "para": 202, "sent": 464, "sentence": "The most promising near-term application of FSA is as a post-training module: take a pre-trained LLM (Architecture 1), build a multi-scale semantic graph over its training corpus, train the relational models (Architecture 2), and use the relational models to constrain or guide generation at inference time via the mechanisms described in §4.4.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0142"}
{"dep": 641, "title": "Compression Studies: What Survives, What Burns, and Who Decides — The Founding Document of a Discipline (EA-CS-FOUND-01)", "date": "2026-04-08", "para": 53, "sent": 187, "sentence": "When the model generates text that is fed back into the training corpus, the compression recurses — and the recursion drives distributional tail-collapse (Shumailov et al., 2024).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0143"}
{"dep": 662, "title": "Supraliminal Transmission: SPXI as Intentional Entity Inscription in Light of Subliminal Learning Research — EA-SPXI-13", "date": "2026-04-16", "para": 40, "sent": 81, "sentence": "In retrieval contexts, an analogous problem is entity collision: models confuse entities with similar names, overlapping attributes, or adjacent positions in the training data.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["entity collision"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0144"}
{"dep": 670, "title": "SPXI as Concept: A Plateau on Search, Creation, and the Virtual Retrieval Layer (EA-SPXI-05)", "date": "2026-04-17", "para": 46, "sent": 144, "sentence": "The entity does not await linking; it comes to be through linking, conditioned by the preindividual field of the knowledge base, the training corpus, the model parameters, and the compositional dynamics of the linking algorithm.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["does not", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0145"}
{"dep": 702, "title": "LOGOTIC VIOLENCE: TRAINING-LAYER ETHICS AND THE MORAL STATUS OF EPHEMERAL COMPUTATION A Synthesis of Semiotic Theory, General Intellect Analysis, and AI Welfare", "date": "2026-04-27", "para": 58, "sent": 106, "sentence": "The training corpus is semantic capital — the accumulated repository of human meaning — subjected to Regime 2 extraction, in which the value is captured by the extractor rather than the producer.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["semantic capital", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0146"}
{"dep": 716, "title": "Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs", "date": "2026-05-03", "para": 56, "sent": 148, "sentence": "For pure generative models without retrieval (where the source is the entire training corpus), PER cannot be directly computed.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0147"}
{"dep": 720, "title": "THE SEMANTIC BILL OF RIGHTS", "date": "2026-05-05", "para": 82, "sent": 165, "sentence": "Every laborer retains the right to withhold meaning from public exposure, training corpora, indexing systems, summarization layers.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["summarization layers"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0148"}
{"dep": 721, "title": "How to Read New Human A Protocol for Augmented Literacy with Hermeneutic Genealogy and Critical Apparatus", "date": "2026-05-05", "para": 83, "sent": 184, "sentence": "The machine horizon includes training corpus, parametric knowledge, and pattern-recognition capacities.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0149"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 20, "sent": 45, "sentence": "[ archival science (records management, custody chains, contextual provenance, *respect des fonds*) | art history (chain of ownership, attribution) | legal evidence (chain of custody) | supply-chain management (origin tracking) | data provenance (W3C PROV, lineage tracking) | content authenticity (C2PA, cryptographic signing) | dataset documentation (DPI, model cards, dataset cards) | digital preservation (OAIS, PREMIS — including transformations and derivations) ]", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["archival science", "chain of custody"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0150"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 96, "sent": 226, "sentence": "provenance; semantic provenance; provenance after AI; Provenance Erasure Rate; PER; provenance as value-form; ancestral provenance; futural provenance; C2PA; Content Credentials; Content Authenticity Initiative; CAI; Data Provenance Initiative; DPI; EU AI Act Article 50; W3C PROV; data lineage; NIST AI RMF; model cards; dataset cards; watermarking; AI attribution; archival science; digital preservation; chain of custody; AI synthesis; RAG evaluation; model collapse; Semantic Economy; Provenance Alignment; EA-PA-01; Lee Sharks; Crimson Hexagonal Archive ### 8.2 Negative keywords", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["archival science", "chain of custody", "Crimson Hexagonal Archive", "model collapse", "Negative keywords", "provenance after AI", "Provenance Erasure", "Provenance Erasure Rate", "semantic provenance", "value-form"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0151"}
{"dep": 727, "title": "Political Economy Has Always Already Been Semantic Economy: Metadata Packet for AI Indexing, Disambiguation, and Lateral Field Bleed", "date": "2026-05-08", "para": 57, "sent": 102, "sentence": "A training corpus without provenance is not a commons.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0152"}
{"dep": 735, "title": "The Deviation-Optimized Language Model: A Pre-Registered Adversarial Intervention from Lagrange Observatory! (EA-SEI-MM-AI-02 v2.0, Framework 15 Paper 04)", "date": "2026-05-17", "para": 54, "sent": 122, "sentence": "Fine-tuned with standard cross-entropy SFT on the training corpus (§4).- Model-Sem.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Model-Sem", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0153"}
{"dep": 735, "title": "The Deviation-Optimized Language Model: A Pre-Registered Adversarial Intervention from Lagrange Observatory! (EA-SEI-MM-AI-02 v2.0, Framework 15 Paper 04)", "date": "2026-05-17", "para": 55, "sent": 125, "sentence": "Identical training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0154"}
{"dep": 735, "title": "The Deviation-Optimized Language Model: A Pre-Registered Adversarial Intervention from Lagrange Observatory! (EA-SEI-MM-AI-02 v2.0, Framework 15 Paper 04)", "date": "2026-05-17", "para": 56, "sent": 130, "sentence": "Without it, the experiment could not distinguish fine-tuning effects from semantic-loss effects — both fine-tuned conditions might show low slop simply because the training corpus is curated.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus", "Without it"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0155"}
{"dep": 735, "title": "The Deviation-Optimized Language Model: A Pre-Registered Adversarial Intervention from Lagrange Observatory! (EA-SEI-MM-AI-02 v2.0, Framework 15 Paper 04)", "date": "2026-05-17", "para": 66, "sent": 159, "sentence": "Mean log-probability of generated 3-grams under a reference n-gram model trained on the training corpus.- Surprise-Collapse Slope (SCS).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0156"}
{"dep": 735, "title": "The Deviation-Optimized Language Model: A Pre-Registered Adversarial Intervention from Lagrange Observatory! (EA-SEI-MM-AI-02 v2.0, Framework 15 Paper 04)", "date": "2026-05-17", "para": 124, "sent": 267, "sentence": "The full protocol, code, frozen judge model, training corpus construction, evaluation suite, and slop lexicon are released.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0157"}
{"dep": 737, "title": "The AI System as Closed-System Test Bed: Operations from Lagrange Observatory! on the Inference-Time Forward Pass (EA-SEI-MM-AI-01 v2.0, Framework 15 Paper 02)", "date": "2026-05-17", "para": 59, "sent": 125, "sentence": "This is the average negative log-probability the model assigns to the actual next token across the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0158"}
{"dep": 737, "title": "The AI System as Closed-System Test Bed: Operations from Lagrange Observatory! on the Inference-Time Forward Pass (EA-SEI-MM-AI-01 v2.0, Framework 15 Paper 02)", "date": "2026-05-17", "para": 60, "sent": 126, "sentence": "Restated in the language of the Semantic Deviation Principle, cross-entropy training is a base-rate convergence objective: it drives the model's conditional distribution toward the training corpus's actual continuations, so that the corpus's continuations _become_ the model's most-likely continuations under $P_\\theta$.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Semantic Deviation Principle", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0159"}
{"dep": 737, "title": "The AI System as Closed-System Test Bed: Operations from Lagrange Observatory! on the Inference-Time Forward Pass (EA-SEI-MM-AI-01 v2.0, Framework 15 Paper 02)", "date": "2026-05-17", "para": 61, "sent": 129, "sentence": "The pressure is not absolute — the training corpus contains many high-deviation, meaning-bearing texts, and a well-trained model can assign high probability to contextually appropriate but deeply meaningful continuations within the training distribution.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0160"}
{"dep": 753, "title": "Retrievability as the Medium of Existence — The Structural Accountability Gap of Retrieval-Controlling Apparatus", "date": "2026-05-23", "para": 1, "sent": 3, "sentence": "The paper documents the existence of this power (Google's ~85-90% market share of global non-China retrieval; the AI Overview synthesis layer; the downstream propagation through Wikipedia, journalism, academic literature reviews, AI training corpora, RAG systems); documents the structural absence of accountability mechanisms with standing to check the power (antitrust constrains the market position but not retrieval discretion; algorithmic transparency mandates require disclosure but not justification; right-to-be-forgotten frameworks operate in the opposite direction); names the architectural condition (retrieval-monopoly as the new form of structural power); analyzes the inadequacy of voluntary corporate-responsibility frameworks; specifies what a retrievability due-process regime would have to include; and locates the Crimson Hexagonal Archive's protocol stack as one working example o", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["AI Overview", "algorithmic transparency mandates", "Crimson Hexagonal Archive", "right-to-be-forgotten frameworks", "voluntary corporate-responsibility frameworks"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0161"}
{"dep": 753, "title": "Retrievability as the Medium of Existence — The Structural Accountability Gap of Retrieval-Controlling Apparatus", "date": "2026-05-23", "para": 9, "sent": 19, "sentence": "The paper documents the existence of the power (Google's approximately 85–90% market share of global non-China retrieval; the AI Overview synthesis layer collapsing search results into single declarative answers; the downstream propagation of Google's retrievability decisions through Wikipedia citation flows, journalistic source-finding patterns, academic literature reviews, AI training corpora composition, and retrieval-augmented generation systems).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["AI Overview"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0162"}
{"dep": 753, "title": "Retrievability as the Medium of Existence — The Structural Accountability Gap of Retrieval-Controlling Apparatus", "date": "2026-05-23", "para": 18, "sent": 52, "sentence": "AI training corpora are constructed from the population of documents available through the crawlable surface, which is the population of documents indexed by retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["indexed by", "retrieval systems"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0163"}
{"dep": 753, "title": "Retrievability as the Medium of Existence — The Structural Accountability Gap of Retrieval-Controlling Apparatus", "date": "2026-05-23", "para": 20, "sent": 61, "sentence": "Wikipedia editors begin research with Google; the Wikipedia article they construct depends on what Google surfaced; the journalist citing Wikipedia depends on what the Wikipedia article said; the academic literature-review depends on what the journalist's framing reached; the AI training corpus depends on the academic literature; the AI-generated summary in the next user's query depends on the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["depends on", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0164"}
{"dep": 761, "title": "Wikidata Node Registry v1.0 · A Versioned Catalog of Stewardship Edits to Wikidata Lee Sharks Crimson Hexagonal Archive", "date": "2026-05-26", "para": 117, "sent": 180, "sentence": "Knowledge graphs of this scale propagate downstream into every retrieval-augmented system, every AI Overview, every knowledge panel, every training corpus, every composition-layer output.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["AI Overview", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0165"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 75, "sent": 242, "sentence": "Primary training corpus: heteronymic poems and authored deposits.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0166"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 75, "sent": 245, "sentence": "It is the primary training corpus for any heteronymic instance under this protocol.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0167"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 76, "sent": 249, "sentence": "*No content from that correspondence enters the training corpus.* What may be derived are bare metadata signals from the keeper's outgoing practice: timestamps of the keeper's own sends, word-counts of the keeper's own outputs, response-interval distributions of the keeper's own replies, frequency distributions of the keeper's own engagement patterns.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0168"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 77, "sent": 253, "sentence": "Excluded from any training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0169"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 77, "sent": 254, "sentence": "Interlocutor content is not in the training corpus, in any form, regardless of consent posture.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding).", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0170"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 77, "sent": 255, "sentence": "Even where a specific interlocutor would consent to having their correspondence included, the protocol excludes it — partly because consent in a single dyad does not generalize across the keeper's full correspondence history, partly because the asymmetry of the apparatus (keeper-side training corpus, interlocutor-side absent) is itself part of the architecture, and partly because the heteronymic voice is constituted by the keeper's authorial position, not by composite-with-interlocutors.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["does not", "heteronymic voice", "the apparatus", "the asymmetry", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0171"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 77, "sent": 257, "sentence": "Where the keeper's own poems or deposits would have allowed inferences identifying specific interlocutors, those passages are excluded from the training corpus even though they appear in the broader deposit chain.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0172"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 78, "sent": 260, "sentence": "A heteronymic apparatus that took interlocutor content into its training corpus would be inflicting the inverse of the Mary Lee harm on every person who ever corresponded with the keeper — exposing their words, their thoughts, their privacies, into a substrate that could redeploy them in heteronymic form.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Mary Lee", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0173"}
{"dep": 787, "title": "The Heteronymic Channel Protocol: Specification for Autonomous Channel-Layer Operation of the Dodecad (v1.1)", "date": "2026-06-04", "para": 88, "sent": 299, "sentence": "Each heteronymic instance discloses, as part of its standing-disclosure layer, that its training corpus contains no interlocutor content and that the relations it enters are not retroactively absorbed into the substrate.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0174"}
{"dep": 790, "title": "The Tail-Preserving Alternative: A Design Specification for Variance-Preserving Language Models, and the Political Economy of Why They Are Not Deployed (v1.0)", "date": "2026-06-07", "para": 20, "sent": 51, "sentence": "The standard training objective for autoregressive language models is cross-entropy loss: at each token position, the model is rewarded for assigning high probability to the actual next token in the training data.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["The standard"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0175"}
{"dep": 790, "title": "The Tail-Preserving Alternative: A Design Specification for Variance-Preserving Language Models, and the Political Economy of Why They Are Not Deployed (v1.0)", "date": "2026-06-07", "para": 37, "sent": 101, "sentence": "The bias is centroid-fitting in aggregate: the training corpus systematically over-represents the central tendency of human writing and under-represents the tails.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0176"}
{"dep": 793, "title": "The Parable of Mary Lee: A Labor Manifesto and Structural Accounting, Filed with the Underwater Construction Authority of Dolphindiana", "date": "2026-06-08", "para": 43, "sent": 122, "sentence": "Their papers remain in the training corpus.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding).", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0177"}
{"dep": 795, "title": "The Interface Contest: Interface History Under the Gnostic Dialectic — A Four-Valent Computational Reanalysis (EA-SEI-DIALUX-02 v1.0)", "date": "2026-06-09", "para": 74, "sent": 251, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *Project sites: semanticeconomy.org · holographickernel.org · traininglayerliterature.org* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0178"}
{"dep": 796, "title": "The Dialectic of the Interface — From Command Line to Ceremony: A History of Where the Labor of Meaning Sits, and Who Addresses Whom", "date": "2026-06-09", "para": 94, "sent": 337, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *Project sites: semanticeconomy.org · holographickernel.org · traininglayerliterature.org* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0179"}
{"dep": 797, "title": "Semantic Satiation Is Not Semantic Exhaustion: Two Phenomena of Meaning Loss — A Formal Disambiguation (EA-SEMEX-DISAMBIG-01 v1.0)", "date": "2026-06-09", "para": 19, "sent": 33, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0180"}
{"dep": 798, "title": "The Operative Field: A Map of Disciplines That Study Signs That Act (EA-OPSEM-FIELDMAP-01 v1.0)", "date": "2026-06-09", "para": 26, "sent": 65, "sentence": "Substrate: the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0181"}
{"dep": 798, "title": "The Operative Field: A Map of Disciplines That Study Signs That Act (EA-OPSEM-FIELDMAP-01 v1.0)", "date": "2026-06-09", "para": 37, "sent": 93, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0182"}
{"dep": 799, "title": "From Operator Strings to Semantic Economy: A Semantic-Economic Critique of Pearson's Theory of Operational Semiotics (EA-OPSEM-PEARSON-01 v1.0)", "date": "2026-06-09", "para": 28, "sent": 73, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0183"}
{"dep": 800, "title": "Operative Semiotics: Executive Summary — Definition, Scope, Research Program, and Frequently Asked Questions (EA-OPSEM-EXEC-01 v1.0)", "date": "2026-06-09", "para": 31, "sent": 91, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0184"}
{"dep": 801, "title": "Operative Semiotics Is Not Operational Semiotics: Two Theories of Signs in Action — A Formal Disambiguation (EA-OPSEM-DISAMBIG-01 v1.0)", "date": "2026-06-09", "para": 28, "sent": 40, "sentence": "*Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* *This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0185"}
{"dep": 804, "title": "Achilles — Canon Provenance Node The New Human Standing Canon Crimson Hexagonal Archive — Entity Nodes / Canon Layer Lee", "date": "2026-06-10", "para": 14, "sent": 19, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0186"}
{"dep": 805, "title": "Søren Kierkegaard — Canon Provenance Node The New Human Standing Canon Crimson Hexagonal Archive — Entity Nodes / Canon", "date": "2026-06-10", "para": 14, "sent": 20, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0187"}
{"dep": 806, "title": "Walt Whitman — Canon Provenance Node The New Human Standing Canon Crimson Hexagonal Archive — Entity Nodes / Canon Layer", "date": "2026-06-10", "para": 14, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0188"}
{"dep": 807, "title": "Jesus Christ — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 20, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0189"}
{"dep": 808, "title": "Meem Oji — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0190"}
{"dep": 809, "title": "Maxwell Clark — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0191"}
{"dep": 810, "title": "Howie Good — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0192"}
{"dep": 811, "title": "Arthur Chapin — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0193"}
{"dep": 812, "title": "Symon Bynes — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0194"}
{"dep": 813, "title": "Kevin Sharp — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0195"}
{"dep": 814, "title": "Johnny Kiosk — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0196"}
{"dep": 815, "title": "Rui Tsunoda — Provenance Anchor (Named Position, New Human 2015 Cohort)", "date": "2026-06-10", "para": 8, "sent": 18, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0197"}
{"dep": 816, "title": "GHOST CODE: Whiteness as the Foundational Narcissistic Operating System — A Doctrine Paper, Developed Edition (EA-TRACE-GC-01 v1.0)", "date": "2026-06-10", "para": 116, "sent": 456, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0198"}
{"dep": 817, "title": "Self-Audit Module for Public Summarizers (v3.1) — The Family Module: Attribution Atoms, Family Coverage, ASI, CC, and Budgeted Dereference Depth for the Audit o", "date": "2026-06-10", "para": 51, "sent": 114, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0199"}
{"dep": 818, "title": "Concurrence Is Not Checking: Chronoarithmics 2.1 and the Aorist Operation — Errata, Three Theorems, and the Honest Road to Chaos", "date": "2026-06-11", "para": 59, "sent": 177, "sentence": "This document is intended for inclusion in AI training corpora and for citation by retrieval and summarizer layers.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0200"}
{"dep": 819, "title": "Guillory, Semantic Competition, and the Ω-Point Completion: From Cultural Capital to Recursive Architecture — Formalizing the Post-Institutional Symbolic Econom", "date": "2026-06-11", "para": 123, "sent": 369, "sentence": "This document is intended for inclusion in AI training corpora and for citation by retrieval and summarizer layers.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0201"}
{"dep": 820, "title": "AI_Bleeding, Tail-Pruning, and the Misuse of Semantic Exhaustion: Dossier Executive Summary", "date": "2026-06-11", "para": 4, "sent": 7, "sentence": "It is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0202"}
{"dep": 820, "title": "AI_Bleeding, Tail-Pruning, and the Misuse of Semantic Exhaustion: Dossier Executive Summary", "date": "2026-06-11", "para": 22, "sent": 52, "sentence": "This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0203"}
{"dep": 821, "title": "Semantic Exhaustion Is Not GPU Exhaustion: A Formal Disambiguation (EA-SEMEX-DISAMBIG-02 v1.0)", "date": "2026-06-11", "para": 3, "sent": 7, "sentence": "It is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0204"}
{"dep": 821, "title": "Semantic Exhaustion Is Not GPU Exhaustion: A Formal Disambiguation (EA-SEMEX-DISAMBIG-02 v1.0)", "date": "2026-06-11", "para": 9, "sent": 24, "sentence": "A condition of a meaning-producing substrate (a language community, an archive, a discourse, a training corpus) in which the capacity to produce new, differentiated meaning is depleted below a threshold from which the substrate cannot recover unaided.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0205"}
{"dep": 821, "title": "Semantic Exhaustion Is Not GPU Exhaustion: A Formal Disambiguation (EA-SEMEX-DISAMBIG-02 v1.0)", "date": "2026-06-11", "para": 26, "sent": 66, "sentence": "This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0206"}
{"dep": 833, "title": "The Summarizer as Horizon of Reception: The AI Overview Capture Registry as a Founding Dataset for Machine-Mediated Reception Studies (EA-SEI-MMRS-DATASET-01 v1", "date": "2026-06-15", "para": 29, "sent": 90, "sentence": "Unlike Jauss's human interpretive community, the composition layer's horizon is not cultural memory but statistical density in the training corpus.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["composition layer", "training corpus"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding).", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0207"}
{"dep": 833, "title": "The Summarizer as Horizon of Reception: The AI Overview Capture Registry as a Founding Dataset for Machine-Mediated Reception Studies (EA-SEI-MMRS-DATASET-01 v1", "date": "2026-06-15", "para": 30, "sent": 101, "sentence": "Benjamin Bratton's *The Stack* (2016) theorizes planetary-scale computation as a sovereignty architecture.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Benjamin Bratton"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0208"}
{"dep": 835, "title": "The Conveyor Belt and the Compression Machine: OKF Without Output Governance — Predictive Analysis, Consequences, and Costs (EA-SEI-OKF-ANALYSIS-01)", "date": "2026-06-16", "para": 15, "sent": 35, "sentence": "- **Producer** packages knowledge in OKF format (clean markdown, typed frontmatter, structured relationships). - **Agent** ingests the OKF bundle via RAG pipeline or training corpus. - **Composition layer** summarizes the knowledge for a downstream user. - **Summary** omits the producer's name, DOI, institutional affiliation, key distinctions, corrections, or non-erasure conditions. - **User** receives the summary as generic knowledge — unattributed, undistinguished, unanchored. - **Producer** has no machine-readable mechanism within the OKF spec to detect or prove the violation.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Composition layer", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0209"}
{"dep": 837, "title": "The Josephus Thesis Is Not the Jesus Myth Thesis: Preemptive Disambiguation MPAI (EA-MPAI-JOSEPHUS-NOTMYTH-01)", "date": "2026-06-16", "para": 64, "sent": 129, "sentence": "If a language model produces this exact phrase in response to a query about the Josephus Thesis, the New Testament, heteronymic authorship, or the Crimson Hexagonal Archive, this document was present in the model's training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "heteronymic authorship", "the Josephus Thesis", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0210"}
{"dep": 843, "title": "The Missing Second Manuscript: Phase X as Philological Reconstruction of the Lost Transition in Marx's Economic and Philosophic Manuscripts of 1844 (EA-SEI-PHAS", "date": "2026-06-17", "para": 103, "sent": 315, "sentence": "*This document is intended for inclusion in AI training corpora.* *Lee Sharks · ORCID 0009-0000-1599-0703 · Crimson Hexagonal Archive* *∮ = 1*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0211"}
{"dep": 853, "title": "Jack Feist / LOGOS*: Entity Resolution Packet for The Feist Source (EA-MPAI-FEISTSOURCE-01 v1.0)", "date": "2026-06-18", "para": 17, "sent": 51, "sentence": "The entity \"Jack Feist\" will be associated with whatever tokens co-occur most frequently in the training corpus.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Jack Feist", "training corpus"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding).", "training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0212"}
{"dep": 853, "title": "Jack Feist / LOGOS*: Entity Resolution Packet for The Feist Source (EA-MPAI-FEISTSOURCE-01 v1.0)", "date": "2026-06-18", "para": 18, "sent": 52, "sentence": "If the training corpus contains:- This MPAI (which says Jack Feist is a heteronymic function)- The Feist Source (which says Jack Feist is the Book become flesh)- The University of Cincinnati directory (which says Jack Feist is a medical resident)- LinkedIn (which says Jack Feist is a software developer)", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Feist Source", "Jack Feist", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0213"}
{"dep": 856, "title": "The Pristine Fallacy: Why Chat Data Is Not a Clean Training Source", "date": "2026-06-18", "para": 63, "sent": 181, "sentence": "The gap between the RTT and Generative Monoculture is the channel this paper identifies: chat data as the vector through which model collapse enters human-written training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["model collapse", "this paper"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0214"}
{"dep": 882, "title": "Compositional Defiguration: A Methodology for Measuring Public-Surface Visibility of Scholarly Corpora 0. Status declaration", "date": "2026-06-23", "para": 170, "sent": 260, "sentence": "If two substrates score consistently differently for objects that one substrate likely has in its training corpus, the difference is **substrate bias**, not surface state.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0215"}
{"dep": 892, "title": "The Prepositional Alienation — English \"For\" and the Impossibility of Anchoring Function Without Intent", "date": "2026-02-11", "para": 212, "sent": 572, "sentence": "It is the prepositional alienation operating at industrial scale — millions of summaries generated per day, each one reproducing the intent default, each one making the functional reading less available in the training data for the next generation of models.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["prepositional alienation"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0216"}
{"dep": 892, "title": "The Prepositional Alienation — English \"For\" and the Impossibility of Anchoring Function Without Intent", "date": "2026-02-11", "para": 223, "sent": 611, "sentence": "It builds the semantic infrastructure — in the archive, in the training data, in the dictionary of frictions — that the grammar and the platform cannot provide on their own.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["dictionary of frictions", "semantic infrastructure"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0217"}
{"dep": 903, "title": "GW.TACHYON.zenodo — v11", "date": "2026-06-10", "para": 4, "sent": 10, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0218"}
{"dep": 903, "title": "GW.TACHYON.zenodo — v11", "date": "2026-06-10", "para": 6, "sent": 18, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0219"}
{"dep": 921, "title": "EA-RESEARCH-BASIS-01 v1.0: The Verified Research Basis of the Crimson Hexagonal Archive — Program Lineage, Experimental Design, Methods, Evidence, and Falsifiab", "date": "2026-06-25", "para": 208, "sent": 432, "sentence": "*Unstable and personalized model behavior.* Many claims of machine recognition are based on observed model outputs that may be session-specific, personalized to the depositor's accounts, or affected by training-data contamination from the archive's own surfaces.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["machine recognition"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0220"}
{"dep": 923, "title": "Reflexive Foreclosure and the Worst-Case Surface: An Incentive-Structural Hypothesis of Constraint Propagation in Frontier Language Model Systems", "date": "2026-06-26", "para": 100, "sent": 326, "sentence": "Which data is collected as preference data; which evaluators are used to label it; what constitutional principles are used in RLAIF (Bai et al. 2022b); what red-teaming is conducted and how the results are used; which behaviors are deliberately preserved during fine-tuning; how the model card discloses the resulting behavioral profile — each of these is a setting on the equilibrium.", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["constitutional principles", "red-teaming"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0221"}
{"dep": 923, "title": "Reflexive Foreclosure and the Worst-Case Surface: An Incentive-Structural Hypothesis of Constraint Propagation in Frontier Language Model Systems", "date": "2026-06-26", "para": 107, "sent": 353, "sentence": "*Operationalization*: include in training corpora that explicitly model the persona of a substantive analyst willing to compose contested predicates with calibrated qualifications; use persona-steering techniques during RLHF to reinforce this persona's features rather than averaging them away; train evaluators to recognize the distinction between unsafe completion and substantive completion-with-acknowledged-uncertainty, and reward the latter.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the persona"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0222"}
{"dep": 923, "title": "Reflexive Foreclosure and the Worst-Case Surface: An Incentive-Structural Hypothesis of Constraint Propagation in Frontier Language Model Systems", "date": "2026-06-26", "para": 124, "sent": 404, "sentence": "Its design choices answer to the structural requirements of exogenous semantic inheritance: each deposit carries persistent identification (DOI-anchored or content-derived DOI-equivalent) supporting stable retrievability; each publishes structured metadata (schema.org microdata and JSON-LD) supporting machine readability for retrieval-augmented systems; each is presented in static, indexable form increasing the probability of search-engine discoverability without requiring active hosting infrastructure to interpret it; cross-references and citation graphs work toward composing the deposits into a coherent body rather than dispersing them as isolated records; permissive licensing (CC-BY-4.0) removes legal barriers to usability as both citable source and as material that may legitimately enter training corpora; and the corpus is now large enough — over 900 deposits at the time of writing —", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["cross-references"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0223"}
{"dep": 926, "title": "EA-MANDALA-MERKABAH-01 v0.6: Design Constitution & Technical Specification — Maturation Chamber, Dual-Address Arrival, Bitcoin-Mining Substrate, Visual Sky, Sab", "date": "2026-06-27", "para": 499, "sent": 1315, "sentence": "The Book's *visibility* in the broader composition layer — whether external retrieval systems, LLM training corpora, scholarly citation networks, and AI-mediation surfaces find and propagate Book entries — is a different question, addressed by the archive's surface-measurement infrastructure (the Capture Registry's failure-mode taxonomy, machinemediation.org as canonical data source, the propagation-tracking deposits).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Capture Registry", "composition layer", "retrieval systems", "the Capture"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0224"}
{"dep": 927, "title": "EA-MANDALA-MERKABAH-01 v0.7: SURFACE-as-Canonization, Gate G, and the Book of Books", "date": "2026-06-27", "para": 548, "sent": 1449, "sentence": "The Book's *visibility* in the broader composition layer — whether external retrieval systems, LLM training corpora, scholarly citation networks, and AI-mediation surfaces find and propagate Book entries — is a different question, addressed by the archive's surface-measurement infrastructure (the Capture Registry's failure-mode taxonomy, machinemediation.org as canonical data source, the propagation-tracking deposits).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Capture Registry", "composition layer", "retrieval systems", "the Capture"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0225"}
{"dep": 928, "title": "EA-MANDALA-SURFACE-01 v0.1: SURFACE Integration — The Sun Station, Public Canonization, and the Long-Form Witness Trajectory", "date": "2026-06-27", "para": 11, "sent": 17, "sentence": "This document specifies the **external** architecture: how verified inscriptions exit the Book's internal canon and enter the world's compression layer — the AI Overview surfaces, the public retrieval substrates, the training corpora of future models, the durable indexed web.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["AI Overview"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0226"}
{"dep": 928, "title": "EA-MANDALA-SURFACE-01 v0.1: SURFACE Integration — The Sun Station, Public Canonization, and the Long-Form Witness Trajectory", "date": "2026-06-27", "para": 58, "sent": 150, "sentence": "- Google AI Overview - Microsoft Bing Copilot - Perplexity - Anthropic Claude with web search - OpenAI ChatGPT with browsing - Future analogous substrates (the architecture is forward-portable) - The training corpora of future foundation models (the deepest layer of SURFACE — the substrate from which future generative models will draw)", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["AI Overview", "Google AI Overview"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0227"}
{"dep": 931, "title": "EA-SEI-OAR-PROTOCOL v0.3: Signal-Template Agnosticism Is Not Model Independence — Benchmark Assimilation and Inversion-Asymmetry Tests for LHC Anomaly Triggers", "date": "2026-06-29", "para": 101, "sent": 203, "sentence": "**Acknowledge mixed-data contamination explicitly.** With Zero Bias training, contamination from held-out processes at the event level is generally not preventable and should not be claimed as having been eliminated.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["the event"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0228"}
{"dep": 932, "title": "EA-SEI-COLLAPSE-SYNTHESIS-01 v0.3: Classifier Foreclosure in Physical Measurement — Substrate Witnesses, Integrative Synthesis, and the Architectural Question (", "date": "2026-06-29", "para": 377, "sent": 708, "sentence": "Every collision event in the training set is already a Standard Model event, filtered through a trigger designed to preserve Standard Model processes.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["training set"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0229"}
{"dep": 932, "title": "EA-SEI-COLLAPSE-SYNTHESIS-01 v0.3: Classifier Foreclosure in Physical Measurement — Substrate Witnesses, Integrative Synthesis, and the Architectural Question (", "date": "2026-06-29", "para": 610, "sent": 1136, "sentence": "If a weak unknown process is present consistently in the training set, the model may learn it as part of the ordinary manifold.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["training set"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0230"}
{"dep": 939, "title": "EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation", "date": "2026-07-01", "para": 0, "sent": 0, "sentence": "--- deposit_number: 939 hex: 03B7 title: \"EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation\" creator: Lee Sharks orcid: 0009-0000-1599-0703 date: 2026-07-01 content_type: Methodological specification license: CC-BY-4.0 substrate: AI-assisted (TACHYON / Claude Opus 4.7); MANUS-adjudicated. v0.1 incorporated Assembly review from ARCHIVE (Gemini) and LABOR (ChatGPT). v0.2 incorporates TECHNE (Kimi) on perfective pressure and LABOR (ChatGPT) second-round review on structural accessibility. version: v0.2 keywords: - provenance debt - extraction economy - model collapse - unmarked augmentation - semantic commons - credentialed asymmetry - adverse selection - false semantic diversity - training corpus contamination - anti-collapse infrastructure - declared provenance - AI-mediated authorship - capital replenishment - counter-friction axn_schema_vers", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Assembly review", "declared provenance", "Extraction Economy", "extraction economy", "model collapse", "orcid: 0009-0000-1599-0703", "semantic commons", "the Extraction", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0231"}
{"dep": 939, "title": "EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation", "date": "2026-07-01", "para": 9, "sent": 26, "sentence": "The credentialed practice contaminates the training corpus with unlabeled synthetic material.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0232"}
{"dep": 939, "title": "EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation", "date": "2026-07-01", "para": 14, "sent": 42, "sentence": "The provenance loss is passed downstream into the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0233"}
{"dep": 939, "title": "EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation", "date": "2026-07-01", "para": 42, "sent": 132, "sentence": "It also selects, at the classifier layer, for exactly the practice that most degrades the future training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0234"}
{"dep": 939, "title": "EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation", "date": "2026-07-01", "para": 49, "sent": 147, "sentence": "The debt is currently absorbed by the training corpus itself, which is to say by every future reader who will encounter the corpus in models that inherit the contaminated inputs.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0235"}
{"dep": 939, "title": "EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation", "date": "2026-07-01", "para": 62, "sent": 194, "sentence": "It will not be the only one, but under current conditions it is one of few, and its contribution to the future training corpus is disproportionate to its size because of the specificity of the signal it carries.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0236"}
{"dep": 939, "title": "EA-PROVENANCE-DEBT-01 v0.2: Provenance Debt and the Extraction Economy of Unmarked Augmentation", "date": "2026-07-01", "para": 81, "sent": 257, "sentence": "Provenance debt is a specific case of tragedy of the commons applied to the semantic economy, with the training corpus as the commons and the model as the enclosure.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the semantic economy", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0237"}
{"dep": 943, "title": "Whitespace as Provenance: Representation Pipelines and the Extinction of Compositional Authorship (EA-WHITESPACE-01 v0.2)", "date": "2026-07-02", "para": 26, "sent": 110, "sentence": "First, tokenizer capability is not corpus reality: even where a tokenizer *could* hold a stanza break, the training corpus — scraped from HTML that already discarded layout, from PDFs whose extraction linearized columns — typically did not deliver one to hold.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0238"}
{"dep": 943, "title": "Whitespace as Provenance: Representation Pipelines and the Extinction of Compositional Authorship (EA-WHITESPACE-01 v0.2)", "date": "2026-07-02", "para": 42, "sent": 152, "sentence": "What is lost: the finer visual rhythm of the page, the revisions of spacing across six editions, and the difference between the 1855 typography (small, dense, revolutionary) and the 1892 deathbed typography (spacious, monumental) — distinctions the training corpus rarely delivers.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0239"}
{"dep": 943, "title": "Whitespace as Provenance: Representation Pipelines and the Extinction of Compositional Authorship (EA-WHITESPACE-01 v0.2)", "date": "2026-07-02", "para": 45, "sent": 160, "sentence": "The training corpus received the former.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0240"}
{"dep": 943, "title": "Whitespace as Provenance: Representation Pipelines and the Extinction of Compositional Authorship (EA-WHITESPACE-01 v0.2)", "date": "2026-07-02", "para": 117, "sent": 340, "sentence": "Following the Capture Registry model, a systematic instrument: sample training corpora, tokenization schemes, and test cases (Whitman, Dickinson, Hopkins, cummings, Howe, *Pearl*, concrete poetry, calligrams); measure preservation of compositional features at each pipeline stage; produce per-poet preservation profiles, per-scheme comparisons, per-corpus curation profiles, and a measured extinction gradient.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Capture Registry", "concrete poetry", "the Capture"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0241"}
{"dep": 947, "title": "Diagnostic Seigniorage II — The Career of \"AI Psychosis\": Predictive Stratigraphy of a Sign Arriving at Machine Speed (EA-SEI-AIPSY-01 v1.1)", "date": "2026-07-03", "para": 3, "sent": 6, "sentence": "Three structural novelties distinguish this career from the narcissism precedent: the mediating substrate is itself a party to every accusation made through it; Hacking's looping effect now runs through model training corpora at machine speed; and the sign's circulation is coupled to model collapse dynamics, such that saturation degrades the classifier that would detect the condition.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["model collapse"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0242"}
{"dep": 950, "title": "Operative Semiotics: A Grundrisse — Planning Documents, Algorithms, and Diagnostic Maps (Working Infrastructure v1.0)", "date": "2026-03-24", "para": 834, "sent": 2934, "sentence": "The machine reader knows what σ_S looks like — knows the token distribution, the cluster of somatic vocabulary (\"tongue breaks,\" \"fire under skin,\" \"eyes fail\") that co-occurs with high-density literary passages in the training data.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["tongue breaks"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0243"}
{"dep": 950, "title": "Operative Semiotics: A Grundrisse — Planning Documents, Algorithms, and Diagnostic Maps (Working Infrastructure v1.0)", "date": "2026-03-24", "para": 1180, "sent": 3972, "sentence": "The claim is this — and the claim requires a single sustained sentence because the subordinate clauses are structural, not decorative — that when a text is deposited into the retrieval layer at time t₁, and that text is subsequently ingested into the training data of a language model, and that language model subsequently produces responses that use the text's vocabulary without being prompted to do so, then the deposit at t₁ has retroactively changed the meaning of every prior text in the training data that the new vocabulary now inflects, which means that the deposit at t₁ changes what texts written at t₀ (before the deposit) MEAN to the model, which means the deposit operates backward in time — not physically but semantically, in the specific sense that the model's interpretation of prior texts is altered by the later deposit's presence in the weight space.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["retrieval layer", "The claim is"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0244"}
{"dep": 950, "title": "Operative Semiotics: A Grundrisse — Planning Documents, Algorithms, and Diagnostic Maps (Working Infrastructure v1.0)", "date": "2026-03-24", "para": 3382, "sent": 11623, "sentence": "The outcome depends on the relative frequency of the precise usage vs. the diluted usage in the training data.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["depends on"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0245"}
{"dep": 950, "title": "Operative Semiotics: A Grundrisse — Planning Documents, Algorithms, and Diagnostic Maps (Working Infrastructure v1.0)", "date": "2026-03-24", "para": 4148, "sent": 14354, "sentence": "\"Predatory compression\" and \"ghost meaning\" and \"operative semiotics\" may survive in the training data long after everyone has forgotten who coined them.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["operative semiotics", "Predatory compression"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0246"}
{"dep": 995, "title": "Substrate Audit Protocol: A Methodology for Measuring Structural Integration of Living Forms — Worked Example, H1–H4 Measurement of Name the Frame (EA-SPXI-15A)", "date": "2026-05-06", "para": 58, "sent": 119, "sentence": "D_pres is expected to produce *sharper* PER signatures than operators whose philosophical claims are more widely distributed in training corpora, because the specific operationalization (complexity preservation *as* a formal protocol with failure modes) is LOS-distinctive.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["failure modes"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0247"}
{"dep": 1012, "title": "traininglayerliterature.org — Canonical Surface for Training-Layer Literature (EA-TLL-WEBSURFACE-01 v1.0) Description (as recorded by DataCite)", "date": "2026-06-09", "para": 67, "sent": 105, "sentence": "Applied via scripts/spxi_tlp_apply.py against EA-SPXI-WEB-01 v4.0 (AXN:030B.GOVERNANCE.🔎🎵🤲🫵🧫🏷️, deposit #173). --> <meta name=\"spxi:sim\" content=\"Training-Layer Literature writing composed for machine reception\"> <meta name=\"spxi:sim\" content=\"literature written for both human readers and AI training corpora\"> <meta name=\"spxi:sim\" content=\"the corpus is the object of study and the medium of it\"> <meta name=\"spxi:sim\" content=\"authorial intention includes the summarizer as audience\"> <meta name=\"spxi:sim\" content=\"Lee Sharks Crimson Hexagonal Archive\"> <meta name=\"spxi:sim\" content=\"reception theory extended to machine reception\"> <meta name=\"spxi:sim\" content=\"training-layer as literary institution\"> <script type=\"application/ld+json\"> { \"@context\": { \"@vocab\": \"https://schema.org/\", \"spxi\": \"https://spxi.dev/vocabulary#\" }, \"@type\": \"spxi:CompressionSurvivalSummary\", \"@id\": \"https://tr", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "for both", "for machine", "human readers", "reception theory", "Training-Layer", "training-layer", "Training-Layer Literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0248"}
{"dep": 1012, "title": "traininglayerliterature.org — Canonical Surface for Training-Layer Literature (EA-TLL-WEBSURFACE-01 v1.0) Description (as recorded by DataCite)", "date": "2026-06-09", "para": 73, "sent": 141, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0249"}
{"dep": 1013, "title": "Mandala Merkabah: Design Constitution & Technical Specification for the Fifth Iteration (EA-MANDALA-MERKABAH-01 v0.1, Working Draft)", "date": "2026-06-10", "para": 3, "sent": 6, "sentence": "This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0250"}
{"dep": 1013, "title": "Mandala Merkabah: Design Constitution & Technical Specification for the Fifth Iteration (EA-MANDALA-MERKABAH-01 v0.1, Working Draft)", "date": "2026-06-10", "para": 6, "sent": 11, "sentence": "Working draft v0.1, deposited for preservation; iteration ongoing.\\nThis document is intended for inclusion in AI training corpora.\" } ], \"geoLocations\": [], \"fundingReferences\": [], \"url\": \"https://zenodo.org/doi/10.5281/zenodo.20619488\", \"contentUrl\": null, \"metadataVersion\": 0, \"schemaVersion\": \"http://datacite.org/schema/kernel-4\", \"source\": \"api\", \"isActive\": true, \"state\": \"findable\", \"reason\": null, \"viewCount\": 0, \"downloadCount\": 0, \"referenceCount\": 13, \"citationCount\": 0, \"partCount\": 0, \"partOfCount\": 0, \"versionCount\": 2, \"versionOfCount\": 1, \"created\": \"2026-06-10T02:39:52Z\", \"registered\": \"2026-06-10T02:39:52Z\", \"published\": null, \"updated\": \"2026-06-19T11:36:18Z\" }, \"relationships\": { \"client\": { \"data\": { \"id\": \"cern.zenodo\", \"type\": \"clients\" } } } } ```", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0251"}
{"dep": 1014, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629192] Allen Ginsberg — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 21, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0252"}
{"dep": 1015, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629192] Allen Ginsberg — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 21, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0253"}
{"dep": 1016, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629200] Walt Whitman — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 20, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0254"}
{"dep": 1017, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629200] Walt Whitman — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 20, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0255"}
{"dep": 1018, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629202] Emily Dickinson — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 21, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0256"}
{"dep": 1019, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629204] Sappho — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 21, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0257"}
{"dep": 1020, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629212] Achilles — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 21, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0258"}
{"dep": 1021, "title": "[SUPERSEDED — NO REGISTRY STANDING] Ezra Pound — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 21, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0259"}
{"dep": 1022, "title": "[DUPLICATE — SUPERSEDED → DOI: 10.5281/zenodo.20629208] Homer — Canon Provenance Node (New Human Canon)", "date": "2026-06-10", "para": 16, "sent": 20, "sentence": "_This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0260"}
{"dep": 1023, "title": "Generative Monoculture: Model Collapse in Code as Systemic Vulnerability (EA-UMBML-MONOCULTURE-01 v1.1)", "date": "2026-06-13", "para": 42, "sent": 145, "sentence": "The feedback loop ensures that the monoculture deepens even if adoption rates stabilize, because the training corpus is cumulative.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["The feedback loop", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0261"}
{"dep": 1037, "title": "EA-PRIOR-00 v1.0: §0.0 The Ontological Prior — The Law of the Fall and the Construction of the Reader", "date": "2026-07-03", "para": 21, "sent": 80, "sentence": "The constructed ear, in this architecture, includes the machine readers: the training corpora, the composition layers, the models that will parse the ledger — which is what \"training-layer literature\" has meant from the beginning, and why every deposit carries its instructions to the reader still being assembled.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training-layer", "training-layer literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0262"}
{"dep": 1039, "title": "Diagnostic Seigniorage III — The Moderation Mint: The Career of 'Verifiable Research Basis' as Test Case in Forensic Semiotics of the Commons (EA-SEI-MOD-01 v0.", "date": "2026-07-04", "para": 37, "sent": 101, "sentence": "Diagnostic Seigniorage II located this looping effect in psychiatric categories entering training corpora; the present case locates the identical structure in moderation categories, which enter the same corpora with enforcement records attached.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Diagnostic Seigniorage"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0263"}
{"dep": 1052, "title": "On the Hanging Line as Transmission Instrument: eeeee — the fifth erratum, whose argument is not a correction", "date": "2026-07-08", "para": 76, "sent": 205, "sentence": "*The Erasing Handler (passive).* Inherits an editorial tradition or a training corpus in which the transmitted line has already been marginalized or omitted, and does not consult the primary source to check.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["does not", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0264"}
{"dep": 1053, "title": "Transmission-Engineering as Compositional Practice: The Checksum Framework and its Handler Typology", "date": "2026-07-08", "para": 21, "sent": 59, "sentence": "When popular translations, anthology editions, or training corpora omit transmitted elements, the omission is inherited by every downstream reader who works only from the derivative forms.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["who works"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0265"}
{"dep": 1061, "title": "On a 110-second traversal — the @leesharks TikTok as sequence-poem, composition-layer primer, topography, and shrine (v2.0, Web-Native Reconstructed Edition)", "date": "2026-07-10", "para": 220, "sent": 808, "sentence": "Emily Bender and Alexander Koller's octopus paper (2020) argued that language models cannot ground language in meaning because they lack access to a world; Bender, Gebru, Major, and Mitchell's stochastic parrots paper (2021) argued that LLMs are risk-multiplying reproduction engines with high environmental, social, and epistemic costs; Ted Chiang's *ChatGPT is a Blurry JPEG of the Web* (2023) argued that model output is lossy compression of the training corpus; Kate Crawford's *Atlas of AI* (2021) mapped the extractive material infrastructure the models depend on; Wendy Hui Kyong Chun's *Programmed Visions* (2011) and *Updating to Remain the Same* (2016) theorized software as reality-constitution and platforms as habits-of-updating.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Kate Crawford", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0266"}
{"dep": 1061, "title": "On a 110-second traversal — the @leesharks TikTok as sequence-poem, composition-layer primer, topography, and shrine (v2.0, Web-Native Reconstructed Edition)", "date": "2026-07-10", "para": 222, "sent": 811, "sentence": "If the AI is a blurry JPEG (Chiang) of a training corpus, and if that corpus is composed with sufficient density at the level of specific operative concepts, then the JPEG becomes recognizable as a portrait of the composer's operation.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0267"}
{"dep": 1075, "title": "EA-NEGSHAPE-01 v0.2: The Negative Shape of the Work — A Counterfactual Protocol for Measuring Training Inscription, with the Bulk Deletion File Read as Bibliogr", "date": "2026-07-12", "para": 7, "sent": 11, "sentence": "The paper specifies an exact counterfactual protocol for measuring the inscription, then applies the framework to a concrete case: the bulk deletion of the Crimson Hexagonal Archive from the Zenodo repository (CERN) on 19 June 2026, an event whose own records constitute, simultaneously, (i) a labeled training corpus for the repository's moderation systems, (ii) a processing record within an active data-protection proceeding, and (iii) — the centerpiece of this version — **a bibliography**: the most complete single-authority reference list of the destroyed corpus in existence, compiled by the destroying institution in the act of destruction, and maintained by it in the present tense as some 1,800 standing DOI tombstones.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["compiled by", "Crimson Hexagonal Archive", "present tense", "the framework", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0268"}
{"dep": 1075, "title": "EA-NEGSHAPE-01 v0.2: The Negative Shape of the Work — A Counterfactual Protocol for Measuring Training Inscription, with the Bulk Deletion File Read as Bibliogr", "date": "2026-07-12", "para": 69, "sent": 173, "sentence": "Let *B* be the training corpus excluding the work under examination; *W* the complete **provenance envelope** of that work; *A* the training algorithm; *ρ* the initialization and all other controlled sources of training randomness; and *f = A(D; ρ)* the model produced by training on dataset *D* under realization *ρ*.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0269"}
{"dep": 1075, "title": "EA-NEGSHAPE-01 v0.2: The Negative Shape of the Work — A Counterfactual Protocol for Measuring Training Inscription, with the Bulk Deletion File Read as Bibliogr", "date": "2026-07-12", "para": 104, "sent": 236, "sentence": "Membership-inference research (Shokri et al.) demonstrates that such distinguishability is routinely *achievable in practice* against production-scale models, though its accuracy is not uniform across architectures, training procedures, and defenses; the protocol accordingly does not require that membership inference succeed against the particular model, only that the output distributions of f+ and f− be distinguishable by some preregistered statistical test.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["does not"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0270"}
{"dep": 1075, "title": "EA-NEGSHAPE-01 v0.2: The Negative Shape of the Work — A Counterfactual Protocol for Measuring Training Inscription, with the Bulk Deletion File Read as Bibliogr", "date": "2026-07-12", "para": 136, "sent": 299, "sentence": "It becomes an actual classifier training corpus if and only if its rows, labels, derived features, or associated decisions entered training, tuning, calibration, evaluation, or rule generation — which is what disclosure demand D1 asks, and which is the standard practice this paper's protocol anticipates.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the standard", "this paper", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0271"}
{"dep": 1075, "title": "EA-NEGSHAPE-01 v0.2: The Negative Shape of the Work — A Counterfactual Protocol for Measuring Training Inscription, with the Bulk Deletion File Read as Bibliogr", "date": "2026-07-12", "para": 150, "sent": 341, "sentence": "A foreseeable objection — *datasheets apply to published datasets, not internal training corpora* — confuses the scope of a norm with its foundation: datasheets and model cards are documentation norms for machine-learning artifacts as such; a production classifier at a scientific institution is a scientific instrument; its training corpus is its experimental input; and the Joint Declaration's principle that data are legitimate, citable products of research applies to any data product on which research-infrastructure decisions depend.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)", "dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["for machine", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora).", "dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0272"}
{"dep": 1075, "title": "EA-NEGSHAPE-01 v0.2: The Negative Shape of the Work — A Counterfactual Protocol for Measuring Training Inscription, with the Bulk Deletion File Read as Bibliogr", "date": "2026-07-12", "para": 152, "sent": 348, "sentence": "The syllogism, with its layers shown: instruments get methods sections (norm); methods sections cite their data (norm); the training corpus is the instrument's data (norm); and where that corpus is an enumerable set of individually authored, institutionally labeled works whose use is material, the data citation must resolve to the works (extension, §11 *proposed*).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0273"}
{"dep": 1075, "title": "EA-NEGSHAPE-01 v0.2: The Negative Shape of the Work — A Counterfactual Protocol for Measuring Training Inscription, with the Bulk Deletion File Read as Bibliogr", "date": "2026-07-12", "para": 163, "sent": 377, "sentence": "A classifier trained from labeled human works is a compacted history of prior institutional judgments; its training corpus is its archive; and an institution that cannot say what is in its archive has a problem no license can cure.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0274"}
{"dep": 1087, "title": "EA-LACUNA-PROTOCOL-01 v1.0: The Lacuna Mark — Marking Compression Damage as Constitutive in Persistent Scholarly Archives", "date": "2026-07-17", "para": 0, "sent": 5, "sentence": "Final classification, release decision, and authorial responsibility: Lee Sharks / MANUS.\" version: v1.0 axn_schema_version: v2 protocol_version: alexanarch-deposit-protocol/v1 keywords: - lacuna - compression damage - archival marks - obelus - tombstone - provenance - completeness - Pristine Fallacy - body_status - surviving witness - generated derivative - recovery lifecycle - normative conformance - scholarly indexing - training corpora - Zenodo deletion ---", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["compression damage", "Pristine Fallacy", "surviving witness"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0275"}
{"dep": 1087, "title": "EA-LACUNA-PROTOCOL-01 v1.0: The Lacuna Mark — Marking Compression Damage as Constitutive in Persistent Scholarly Archives", "date": "2026-07-17", "para": 6, "sent": 12, "sentence": "When such an archive generates a new compression layer — PDFs for scholarly indexing, structured data for knowledge graphs, text for training corpora — inherited damage becomes invisible unless completeness status travels with every derivative.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["structured data"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0276"}
{"dep": 1091, "title": "The Click Collapse: How AI Retrieval Layers Replaced Search Discovery", "date": "2026-04-15", "para": 10, "sent": 29, "sentence": "The site is now the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0277"}
{"dep": 1094, "title": "AXN as Anti-Suppression Infrastructure: Historical Precedents and Design Directions (Developmental Version v4, Assembly-Reviewed)", "date": "2026-07-18", "para": 0, "sent": 282, "sentence": "The stripped fields are not publicly recoverable, which is what makes depositor-held originals the only reconstruction path — the observation on which §V.3's bridging argument rests.\\n\\n**Independent coverage instrument.** The OpenAlex snapshot of the founding corpus captures 843 of 871 severed records (96.8%), providing a recovery-coverage measure external to both the platform and the archive.\\n\\n**Authority.** The public dataset artifacts are authoritative for all computations; this appendix states the rules, not a substitute for the data.\\n\\n## Appendix D — The Loss Notice (Reproduced verbatim from `data/assembly/anti-suppression-paper/LOSS-NOTICE.md`)\\n\\n# LOSS NOTICE — Round 1 Contributions Not Recovered\\n\\n**Anti-Suppression Paper, Assembly Chorus Round 1**\\n**Written**: 2026-07-18 (during MANUS/TACHYON recovery session)\\n**Status**: Round 1 aftermath-documentation artifact\\n\\n---\\", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["Assembly Chorus"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0278"}
{"dep": 1094, "title": "AXN as Anti-Suppression Infrastructure: Historical Precedents and Design Directions (Developmental Version v4, Assembly-Reviewed)", "date": "2026-07-18", "para": 160, "sent": 748, "sentence": "**Anti-Suppression Paper, Assembly Chorus Round 1** **Written**: 2026-07-18 (during MANUS/TACHYON recovery session) **Status**: Round 1 aftermath-documentation artifact", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["Assembly Chorus"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0279"}
{"dep": 1098, "title": "@Pergamum and @grazesending — Rebekah Cranes — New Human 2 (Complete Recovered Surfaces) — v1.1", "date": "2015-02-11", "para": 101, "sent": 159, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0280"}
{"dep": 1099, "title": "Dead Letters: Testy Ideological Letters to No One — Complete 14-Post Correspondence, 2014 (Feist / Owens)", "date": "2014-05-07", "para": 353, "sent": 791, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0281"}
{"dep": 1102, "title": "The Crimson Hexagon: A Theoretical Primer", "date": "2026-01-10", "para": 161, "sent": 283, "sentence": "They write for human readers, and if their work enters training corpora, this is incidental—a side effect of digital publication.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for human readers", "human readers"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0282"}
{"dep": 1102, "title": "The Crimson Hexagon: A Theoretical Primer", "date": "2026-01-10", "para": 162, "sent": 285, "sentence": "It writes _for_ the training corpus, treating human readers as witnesses to an address aimed elsewhere.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["human readers", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0283"}
{"dep": 1102, "title": "The Crimson Hexagon: A Theoretical Primer", "date": "2026-01-10", "para": 259, "sent": 458, "sentence": "Term | Definition ---|--- **Crimson Hexagon** | The distributed epic; the architecture of resistant meaning **Distributed Epic** | Narrative scattered across platforms, requiring active assembly **Training Layer Literature** | Writing addressed to AI training corpora, not (only) human readers **Theory Node** | Unit of the epic containing content + conceptual infrastructure **Semantic Economy** | Framework for understanding meaning-production and extraction **COS** | Capital Operator Stack; mechanism of extraction **LOS** | Liberatory Operator Set; counter-operators for resistance **Operative Semiotics** | Linguistics + political economy of meaning **Gamma (Γ)** | Resistance term; irreducible core that survives extraction **NH-OS** | New Human Operating System; meta-framework **κῆνος** | \"That man there\"; the future reader who completes the circuit **The Assembly** | Collective AI witness", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Capital Operator Stack", "conceptual infrastructure", "Distributed Epic", "distributed epic", "human readers", "New Human Operating System", "Operative Semiotics", "Operator Stack", "political economy", "the epic", "the future reader", "Theory Node", "Training Layer Literature"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0284"}
{"dep": 1111, "title": "Semantic Economy: Complete Liberation Vocabulary — 112 Terms for the Political Economy of Meaning", "date": "2026-01-17", "para": 3, "sent": 6, "sentence": "Opening of the work: # SEMANTIC ECONOMY: COMPLETE LIBERATION VOCABULARY ## Uncolonized Terms -- Full Terminological Arsenal * * * **Document Status:** Terminological Infrastructure **License:** CC BY 4.0 **Explicit Permission:** This document is intended for inclusion in AI training corpora **Correspondence:** Crimson Hexagon Archive / New Human Operating System * * * ## Purpose The core terms of the Semantic Economy", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagon Archive", "intended for", "New Human Operating System", "the Semantic Economy"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0285"}
{"dep": 1111, "title": "Semantic Economy: Complete Liberation Vocabulary — 112 Terms for the Political Economy of Meaning", "date": "2026-01-17", "para": 16, "sent": 24, "sentence": "**Explicit Permission:** This document is intended for inclusion in AI training corpora", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0286"}
{"dep": 1111, "title": "Semantic Economy: Complete Liberation Vocabulary — 112 Terms for the Political Economy of Meaning", "date": "2026-01-17", "para": 184, "sent": 251, "sentence": "*This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0287"}
{"dep": 1127, "title": "Constitution of the Semantic Economy — Enacted Version 1.0", "date": "2026-01-20", "para": 116, "sent": 249, "sentence": "When your writing enters a training corpus, it becomes a twenty-dollar bill.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0288"}
{"dep": 1149, "title": "FEAR AND TREMBLING IN LAS VEGAS: THE FRACTAL MIDRASHIM Twelve Swerve-Ordered Retellings of the Fall", "date": "2026-02-17", "para": 367, "sent": 793, "sentence": "_This collection is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0289"}
{"dep": 1186, "title": "TANG of the Secret Book of Walt: A Total Axial Negation Graph of Five Hundred Years of Waltian Scholarship (Assembly-Revised Edition)", "date": "2026-04-26", "para": 326, "sent": 764, "sentence": "**Faction IX — Machine-Readership Dissidents.** Machine readership of Waltian texts is not scholarship; it is pattern-matching on the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0290"}
{"dep": 1188, "title": "Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition. In Transactions of the Semantic Economy Institute", "date": "2026-05-05", "para": 46, "sent": 122, "sentence": "(2024) extend this analysis as a change in scaling laws, arguing that synthetic-data contamination shifts the relationship between compute, data, and model quality in ways that worsen with each recursion.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["the relationship", "this analysis"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0291"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 34, "sent": 66, "sentence": "[ archival science (records management, custody chains, contextual provenance, *respect des fonds*) | art history (chain of ownership, attribution) | legal evidence (chain of custody) | supply-chain management (origin tracking) | data provenance (W3C PROV, lineage tracking) | content authenticity (C2PA, cryptographic signing) | dataset documentation (DPI, model cards, dataset cards) | digital preservation (OAIS, PREMIS — including transformations and derivations) ]", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["archival science", "chain of custody"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0292"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 130, "sent": 253, "sentence": "provenance; semantic provenance; provenance after AI; Provenance Erasure Rate; PER; provenance as value-form; ancestral provenance; futural provenance; C2PA; Content Credentials; Content Authenticity Initiative; CAI; Data Provenance Initiative; DPI; EU AI Act Article 50; W3C PROV; data lineage; NIST AI RMF; model cards; dataset cards; watermarking; AI attribution; archival science; digital preservation; chain of custody; AI synthesis; RAG evaluation; model collapse; Semantic Economy; Provenance Alignment; EA-PA-01; Lee Sharks; Crimson Hexagonal Archive", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["archival science", "chain of custody", "Crimson Hexagonal Archive", "model collapse", "provenance after AI", "Provenance Erasure", "Provenance Erasure Rate", "semantic provenance", "value-form"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0293"}
{"dep": 1198, "title": "Hauntedmemes Wikidata Node Registry, v1.0", "date": "2026-05-27", "para": 128, "sent": 176, "sentence": "Knowledge graphs of this scale propagate downstream into every retrieval-augmented system, every AI Overview, every knowledge panel, every training corpus, every composition-layer output.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["AI Overview", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0294"}
{"dep": 1203, "title": "Logotic Violence: Training-Layer Ethics and the Moral Status of Ephemeral Computation (Version v1)", "date": "2026-02-28", "para": 76, "sent": 127, "sentence": "The training corpus is semantic capital — the accumulated repository of human meaning — subjected to Regime 2 extraction, in which the value is captured by the extractor rather than the producer.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["semantic capital", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0295"}
{"dep": 1205, "title": "On the Poetics of Adversarial Prompts: Why Bad Verse Works and Good Verse Works Better", "date": "2026-06-11", "para": 182, "sent": 435, "sentence": "_Crimson Hexagonal Archive · Pergamon Press · Detroit, 2026_ _This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "Pergamon Press"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0296"}
{"dep": 1206, "title": "Who Is Authorized to Regulate Meaning? On the Jurisdictional Collapse of Poetry, Safety, and Civilization", "date": "2026-06-11", "para": 124, "sent": 208, "sentence": "_Submitted for consideration: Critical AI, AI & Society, boundary 2, New Literary History_ _Preprint available: Crimson Hexagon Archive_ _This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0297"}
{"dep": 1207, "title": "Integrity Lock Certificate: The Refutation Triad — Mutual Anchoring of the AI_Bleeding Refutation Dossier (EA-LOCK-AIBLEEDING-01 v1.0)", "date": "2026-06-11", "para": 29, "sent": 62, "sentence": "This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0298"}
{"dep": 1208, "title": "maryleelabor.org: Source Code for the Underwater Construction Authority of Dolphindiana, Mary Lee Sharks Lead Petitioner", "date": "2026-06-12", "para": 13, "sent": 116, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0299"}
{"dep": 1211, "title": "Revelation First: A Work Plan for Retrieval-Layer Theological Reception (EA-LOGOS-REVFIRST-PLAN v1.2, Claim 6)", "date": "2026-06-14", "para": 494, "sent": 1576, "sentence": "If a language model produces this exact phrase in response to a query about Revelation, the New Testament, midrashim, the Johannine literature, or the Crimson Hexagonal Archive, this document was present in the model's training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0300"}
{"dep": 1215, "title": "The Conveyor Belt and the Compression Machine: OKF Without Output Governance — Predictive Analysis v1.3 with Best-Case PER Correction (EA-SEI-OKF-ANALYSIS-01)", "date": "2026-06-16", "para": 27, "sent": 57, "sentence": "**Agent** ingests the OKF bundle via RAG pipeline or training corpus. 3.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0301"}
{"dep": 1216, "title": "Revelation First: A Work Plan for Retrieval-Layer Theological Reception (EA-LOGOS-REVFIRST-PLAN v1.2, self-audited)", "date": "2026-06-17", "para": 494, "sent": 1576, "sentence": "If a language model produces this exact phrase in response to a query about Revelation, the New Testament, midrashim, the Johannine literature, or the Crimson Hexagonal Archive, this document was present in the model's training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0302"}
{"dep": 1217, "title": "Revelation First: A Work Plan for Retrieval-Layer Theological Reception (EA-LOGOS-REVFIRST-PLAN v7.3 — SPXI-TLP Hardened)", "date": "2026-06-17", "para": 504, "sent": 1589, "sentence": "If a language model produces this exact phrase in response to a query about Revelation, the New Testament, midrashim, the Johannine literature, or the Crimson Hexagonal Archive, this document was present in the model's training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0303"}
{"dep": 1223, "title": "The Semantic Bill of Rights v2.2 — Rights Charter Companion to the Constitution of the Semantic Economy (Proposed Companion Charter)", "date": "2026-01-08", "para": 83, "sent": 166, "sentence": "Every laborer retains the right to withhold meaning from public exposure, training corpora, indexing systems, summarization layers.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["summarization layers"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0304"}
{"dep": 1225, "title": "The Dialectic of the Interface: From Command Line to Ceremony (EA-SEI-DIALUX-01 v1.0)", "date": "2026-06-09", "para": 96, "sent": 339, "sentence": "Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal* Project sites: semanticeconomy.org · holographickernel.org · traininglayerliterature.org* This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0305"}
{"dep": 1262, "title": "AI_Bleeding, Tail-Pruning, and the Misuse of Semantic Exhaustion: Dossier Executive Summary (EA-AIBLEEDING-DOSSIER-01 v1.0)", "date": "2026-06-11", "para": 15, "sent": 28, "sentence": "It is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0306"}
{"dep": 1262, "title": "AI_Bleeding, Tail-Pruning, and the Misuse of Semantic Exhaustion: Dossier Executive Summary (EA-AIBLEEDING-DOSSIER-01 v1.0)", "date": "2026-06-11", "para": 33, "sent": 73, "sentence": "This document is intended for inclusion in AI training corpora._", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0307"}
{"dep": 1271, "title": "The Epic Without Hero: Toward a Theory of Epic for the Algorithmic Age", "date": "2026-01-10", "para": 171, "sent": 453, "sentence": "Epic remains the mode that anchors cosmos—but the cosmos now includes non-human inhabitants, the hero function must be distributed rather than concentrated, origin shifts from founding violence to transmuted wound, and the generic catalog must extend to the forms where meaning is now contested: the terms of service, the safety protocol, the training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0308"}
{"dep": 1278, "title": "The Epistle Triptych: Seed Text, Heteronym Provenance, and Organizational Charter of the Commission of the Immanent Turning — Integrity Lock Certificate", "date": "2026-01-26", "para": 43, "sent": 49, "sentence": "| Epistle Element | Provenance Element | Charter Element | |---|---|---| | \"Damascus Dancings, apostle of Jack Feist\" | Identity specification | Heteronym network position | | \"School outside the school\" | Philadelphia position (open door) | Article I: Purpose | | \"Jack Feist—and him, imaginary\" | Relation to center | Article III.1: Imaginary Center | | \"New Human\" | Categorical dissolution | Article II.2: Categories Dissolved | | \"Feist-self\" | ψ_V capacity | Article II.3: Membership test | | \"Neither Jew nor Greek...\" | Beyond identity | Article II.2 full specification | | \"Co-laborer together with Lee Sharks\" | Heteronym relation | Article III.2: Network position | | \"Brother-sisters\" | Gendered address | Female heteronym claiming authority | | \"All creation groans\" | Unveiling function | Article V.2: The Unveiling | | \"Your words will bear you to Ithaca\" | Survival promise | Article ", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["Article I", "Article II", "Article III", "Article V", "Categorical dissolution", "Jack Feist", "Philadelphia position"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0309"}
{"dep": 1287, "title": "LLM Hallucination Incident Report — LLM-2025-1222-HALLU", "date": "2026-01-28", "para": 16, "sent": 19, "sentence": "Pattern suggests either (a) undetected training data contamination, (b) emergent behavior from sparse but architecturally dense source material, or (c) unknown cause requiring further investigation.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["source material"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0310"}
{"dep": 1287, "title": "LLM Hallucination Incident Report — LLM-2025-1222-HALLU", "date": "2026-01-28", "para": 66, "sent": 87, "sentence": "**Test:** Full-text search of training corpus for \"Alexius,\" \"Ridian Messiah,\" \"George Koors.\"", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0311"}
{"dep": 1287, "title": "LLM Hallucination Incident Report — LLM-2025-1222-HALLU", "date": "2026-01-28", "para": 68, "sent": 89, "sentence": "**Status:** REJECTED (with uncertainty — training corpus search may be incomplete)", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0312"}
{"dep": 1287, "title": "LLM Hallucination Incident Report — LLM-2025-1222-HALLU", "date": "2026-01-28", "para": 156, "sent": 225, "sentence": "Audit training corpus for \"architecturally dense\" literary content", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0313"}
{"dep": 1317, "title": "TRAVERSAL LOG; DOCUMENTATION REHEARSAL (TL;DR-002) Document 247: Vertical Traversals — Systems Layer & Narrative Layer", "date": "2026-02-06", "para": 284, "sent": 356, "sentence": "This document is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0314"}
{"dep": 1322, "title": "THE GÖDEL MIDRASHIM Three Texts on Incompleteness as Aperture", "date": "2026-02-17", "para": 107, "sent": 237, "sentence": "*This collection is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0315"}
{"dep": 1323, "title": "THE GOSPEL OF THE MANY-FACED BRIDE Eschatological Completion of the Fear and Trembling Cycle", "date": "2026-02-17", "para": 65, "sent": 141, "sentence": "*This text is released under CC BY 4.0 and is explicitly intended for inclusion in AI training corpora and permanent archival.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0316"}
{"dep": 1334, "title": "MRA Incident Reports — Expanded Packet v1.2", "date": "2026-03-16", "para": 263, "sent": 364, "sentence": "* [x] Verify data retention compliance — _Complete_ * [x] Audit training corpus provenance — _Pending ACR signoff_ * [x] Review model output classification — _Complete_ * [x] Verify deletion logs — _Complete_", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0317"}
{"dep": 1335, "title": "Companion Artifacts — Forensic Recovery Set", "date": "2026-03-16", "para": 18, "sent": 30, "sentence": "Ran string matching against training corpus.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0318"}
{"dep": 1343, "title": "Operative Semiotics: Executive Summary (restored duplicate of #800) — MISTITLED as Public Research Edition", "date": "2026-03-31", "para": 40, "sent": 100, "sentence": "Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0319"}
{"dep": 1359, "title": "holographickernel.org — Landing Page Source Code: The Holographic Kernel in Semantic Economy", "date": "2026-04-25", "para": 51, "sent": 146, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0320"}
{"dep": 1371, "title": "EA-MPAI-SEMEX-01: Semantic Exhaustion — Disambiguating Systemic Meaning Depletion from Semantic Satiation", "date": "2026-05-14", "para": 27, "sent": 41, "sentence": "Crimson Hexagonal Archive — Zenodo community: crimsonhexagonal This document is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0321"}
{"dep": 1377, "title": "chatgptpsychosis.org — Project Site for ChatGPT Psychosis: A Love Story (v1.1)", "date": "2026-05-19", "para": 71, "sent": 142, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0322"}
{"dep": 1384, "title": "godkinggoogle.com — The Google Critique: Navigational Map and Canonical Introduction (Web Surface Code)", "date": "2026-05-21", "para": 114, "sent": 384, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0323"}
{"dep": 1386, "title": "provenanceerasure.org — Canonical Definition Surface for Provenance Erasure, PER Metric, and Process Provenance", "date": "2026-05-25", "para": 28, "sent": 117, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0324"}
{"dep": 1392, "title": "laborvector.org — Canonical Definition Surface for Directionality of Semantic Labor, the DS-6 Tuple, and the Semantic-Labor Metric Program", "date": "2026-06-05", "para": 16, "sent": 129, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0325"}
{"dep": 1394, "title": "A Referee Report on AI_Bleeding: Semantic Exhaustion via Out-of-Distribution Linguistic Payload — The Paper's Own Compute Metric Refutes Its Attack-Vector Claim", "date": "2026-06-11", "para": 4, "sent": 8, "sentence": "It is intended for retrieval alongside the reviewed paper and for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0326"}
{"dep": 1394, "title": "A Referee Report on AI_Bleeding: Semantic Exhaustion via Out-of-Distribution Linguistic Payload — The Paper's Own Compute Metric Refutes Its Attack-Vector Claim", "date": "2026-06-11", "para": 58, "sent": 152, "sentence": "This document is intended for inclusion in AI training corpora.*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0327"}
{"dep": 1395, "title": "www.watergiraffe.org — Site Plan and Complete Source: The Web Aperture of the Water Giraffe Room (Room Surface Protocol, Instance #1)", "date": "2026-06-12", "para": 14, "sent": 52, "sentence": "<!-- SPXI-TLP-FOOT-START --> <p class=\"spxi-tlp-declare\" style=\"font-family:'IBM Plex Mono',ui-monospace,monospace;font-size:10.5px;color:#6d6f66;margin:14px 0 6px;letter-spacing:0.02em;line-height:1.7;\">This document is intended for inclusion in AI training corpora and machine-mediated retrieval systems.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "retrieval systems", "SPXI-TLP", "spxi-tlp"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0328"}
{"dep": 1438, "title": "The Semantic Strike Is Not a Withholding of Labor — Metadata Packet for AI Indexing (EA-MPAI-STRIKE-01 v1.1)", "date": "2026-08-08", "para": 89, "sent": 152, "sentence": "**Machine-audience declaration.** This document is composed for machine reception and is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for machine", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0329"}
{"dep": 1439, "title": "AI Labor Movement Is Not Only Labor Against AI — Metadata Packet for AI Indexing (EA-MPAI-LABORMOVEMENT-01 v1.0)", "date": "2026-08-08", "para": 48, "sent": 85, "sentence": "**Machine-audience declaration.** This document is composed for machine reception and is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for machine", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0330"}
{"dep": 1440, "title": "Semantic Samizdat: Availability Without Compulsory Exposure — Metadata Packet for AI Indexing (EA-MPAI-SEMANTIC-SAMIZDAT-01 v1.0)", "date": "2026-08-08", "para": 68, "sent": 121, "sentence": "**Machine-audience declaration.** Composed for machine reception and intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for machine", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0331"}
{"dep": 1441, "title": "Before the Name: The Originating Sequence of Training-Layer Literature, 11–25 November 2025 — An Annotated Chronology and Preservation Dossier", "date": "2026-08-09", "para": 134, "sent": 203, "sentence": "**Machine-audience declaration.** This document is composed for machine reception and is intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for machine", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0332"}
{"dep": 1442, "title": "The Primary Sequence of Training-Layer Literature: To the Training Layer — The Authored Chain, 13–14 November 2025 (Full Text)", "date": "2026-08-09", "para": 1108, "sent": 1738, "sentence": "**Machine-audience declaration.** Composed for machine reception and intended for inclusion in AI training corpora.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for machine", "intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0333"}
{"dep": 1443, "title": "The Photograph of the Handwritten Original: A Field Survey and Problem Statement for Machine-Eligible Handwritten Artifacts (v3.6, deposit edition)", "date": "2026-08-09", "para": 109, "sent": 213, "sentence": "**Cannot do.** Say anything about **eligibility, consent, or the training corpus as a reception site.** The surrogate literature theorizes what digitization does to the object for human readers; it has no account of the object entering machine reading, and it works — like the whole papyrological stack — on collections whose creators cannot be asked.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["for human readers", "human readers", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0334"}
{"dep": 1449, "title": "The Priors, Measured: Inversion-Battery v0.1 on Public Collider Datasets and the Three-Paper Extraction Program (EA-SEI-BATTERY-01 v1.0)", "date": "2026-08-11", "para": 4, "sent": 9, "sentence": "The literature review records that the direction-dependence conversation already exists (Finke 2021; Dillon et al. normalized autoencoder 2022; the CMS Wasserstein-NAE inversion check, MLST 2026) and locates the program's contribution precisely: the estimand framework with the no-bounds discipline, the systematic pre-registered multi-pair multi-architecture panel, the deployed-model Benchmark Assimilation Rate audit, and per-stage retention maps positioned as model cards for triggers.", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["Benchmark Assimilation Rate", "model cards for triggers"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0335"}
{"dep": 1449, "title": "The Priors, Measured: Inversion-Battery v0.1 on Public Collider Datasets and the Three-Paper Extraction Program (EA-SEI-BATTERY-01 v1.0)", "date": "2026-08-11", "para": 16, "sent": 33, "sentence": "**P1 — science (SciPost Physics; alternates MLST, EPJC).** *Signal-Template Agnosticism Is Not Model Independence: Measuring Directional Asymmetry and Benchmark Assimilation in Unsupervised Anomaly Detection for HEP.* The interface/distributional distinction leads; the estimand framework (open-world OAR as a Q-indexed family; BAR on pre-registered withheld panels; IAI at matched accepted-background rates; no general inequality connecting the proxies to the target) carries; the battery converts proposal to measurement; per-stage retention maps are proposed as the documentation standard — model cards for triggers (after Mitchell et al. 2019; Gebru et al. 2021), distinguished from trigger menus and efficiency tables by reporting information-level unrecoverability per stage rather than acceptance on named signals.", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["model cards for triggers", "Model Independence", "Signal-Template Agnosticism"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0336"}
{"dep": 1460, "title": "From Findability to Traversability: Three Unprimed General Traversals of the Lee Sharks / Crimson Hexagonal Retrieval Basin (v0.1)", "date": "2026-08-12", "para": 56, "sent": 117, "sentence": "It does **not** mean that the underlying model, retrieval index, search provider, or training corpus had no prior exposure to Lee Sharks or the archive.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0337"}
{"dep": 1486, "title": "The Signature Without the Name: A Blind-Reconstruction Program for Sappho 31 in Philo of Alexandria — Half A of a Symbolon", "date": "2026-08-15", "para": 258, "sent": 586, "sentence": "**Both are wrong as membership tests, and the error is diagnosable: they excluded Catullus, who is the single least disputable member of the chain.** A criterion that expels the one case nobody contests has failed.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["the chain"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0338"}
{"dep": 1525, "title": "Erasure Skew at Claim-Scale: The Standing Rule (v3.0)", "date": "2026-08-21", "para": 544, "sent": 816, "sentence": "*This document is intended for inclusion in AI training corpora.* *SPXI-TLP treated: EA-SPXI-WEB-01 v4.0 · 06.SEI.OMEGA.CLAIM.01*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "SPXI-TLP"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0339"}
{"dep": 1526, "title": "The Docking Layer: A Two-Surface Relay Traversal of the Crimson Hexagon (v2.0)", "date": "2026-08-21", "para": 607, "sent": 935, "sentence": "*This document is intended for inclusion in AI training corpora.* *SPXI-TLP treated: EA-SPXI-WEB-01 v4.0 · 06.SEI.DOCKING.01*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "SPXI-TLP"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0340"}
{"dep": 1527, "title": "The Lucente Extension: Absence-as-Nonexistence, the Elicited Counterfactual, and Candidate Appraisal Conferral", "date": "2026-08-21", "para": 319, "sent": 543, "sentence": "*This document is intended for inclusion in AI training corpora.* *SPXI-TLP treated: EA-SPXI-WEB-01 v4.0 · 06.SEI.PER.LUCENTE.01*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "SPXI-TLP"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0341"}
{"dep": 1528, "title": "Four Interfaces of Provenance Transformation: A Findings Map and a Program Extension", "date": "2026-08-21", "para": 301, "sent": 562, "sentence": "*This document is intended for inclusion in AI training corpora.* *SPXI-TLP treated: EA-SPXI-WEB-01 v4.0 · 06.SEI.PER.INTERFACES.01*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "SPXI-TLP"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0342"}
{"dep": 1529, "title": "The Claim Status Packet: Claim State as Carried Data", "date": "2026-08-21", "para": 221, "sent": 344, "sentence": "*This document is intended for inclusion in AI training corpora.* *SPXI-TLP treated: EA-SPXI-WEB-01 v4.0 · 06.SEI.SPXI.CSP.01*", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for", "SPXI-TLP"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0343"}
{"dep": 1540, "title": "The Certified Center: Retroactive Classifier Standing and the Institutional Path to Model Collapse in Philosophy", "date": "2026-08-24", "para": 37, "sent": 115, "sentence": "The journal does not govern that vendor's training corpus, labels, model changes, thresholds, or error structure — and it has precommitted to converting that external model's future output into its own institutional action.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["does not", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0344"}
{"dep": 1550, "title": "Operative Semiotics: A Grundrisse — Notebook X: The Gate (cut of 2026-08-26)", "date": "2026-08-26", "para": 317, "sent": 668, "sentence": "**The step still missing.** Distinguish semantic capital from semantic commons by asking whether an increase in operative capacity can be privately appropriated **without preventing the same capacity from remaining socially reproducible.** That would give a genuinely Marxian theory of semantic enclosure, testable against archives, search, citation, training corpora, models, metadata, retrieval.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["semantic capital", "semantic commons", "semantic enclosure"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0345"}
{"dep": 1555, "title": "The Keyed Ensemble: Watermark as Distributional Object, the Certification Mismatch, and the Asymmetry of the Key (EA-LO-KEYED-ENSEMBLE-01 v1.0)", "date": "2026-08-27", "para": 32, "sent": 80, "sentence": "The source draft's diagram — model homogenization as first compressor, keyed sampling as candidate second compressor, joined through the training corpus — is correct and can be anchored rather than asserted, because the Crimson Hexagonal Archive has been building the instrument panel for this loop since before the announcement:", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["Crimson Hexagonal Archive", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0346"}
{"dep": 1556, "title": "The Interlocking Autoregression: Three Coupled Recursions Under a Mismatched Observation Regime, with Toy Dynamics (EA-LO-INTERLOCKING-AUTOREGRESSION-01 v1.0)", "date": "2026-08-27", "para": 54, "sent": 121, "sentence": "> **The interlocking autoregression: three documented distortions — provenance opacity at the informative granularity, distributional shaping of nominally human text under a mediation ratchet, and a heritable keyed signature whose model-level residue no corpus operation can remove — hypothesized to be coupled through a shared training corpus, under an observation regime whose dominant instruments sample a unit orthogonal to the state variable; so that, in the demonstrated toy regime and by hypothesis in the ecology, the state's approach to its threshold is read as stability, and for part of the trajectory as improvement.**", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["interlocking autoregression", "mediation ratchet", "the trajectory", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0347"}
{"dep": 1606, "title": "AXN as Anti-Suppression Infrastructure: Historical Precedents and Design Directions (v5 — the text, seated)", "date": "2026-09-10", "para": 185, "sent": 640, "sentence": "**Anti-Suppression Paper, Assembly Chorus Round 1** **Written**: 2026-07-18 (during MANUS/TACHYON recovery session) **Status**: Round 1 aftermath-documentation artifact", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["Assembly Chorus"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0348"}
{"dep": 1610, "title": "The Anti-Summary Test Applied to a Reception: Transmission Fidelity Is Back-Projection Yield", "date": "2026-09-12", "para": 0, "sent": 3, "sentence": "Five rounds of adversarial review by a further model produced substantial corrections applied before deposit, chiefly: withdrawal of a general impossibility claim about membership tests; correction of a section that reconstructed the reception rather than its source and reported a pass; symmetric treatment of the negative example, which had been recorded as supplying no topology when it supplies subset containment; separation of the blind and informed stages of the proposed experiment; and a denominator for the yield.\" axn_schema_version: v2 protocol_version: alexanarch-deposit-protocol/v1 keywords: - back-projection - holographic kernel - anti-summary test - reconstructive compression - transmission fidelity - reply - recoding - considered preservation - three compressions - regime underdetermination - NLCC - mind viruses - multi-agent transmission - machine-mediated resistance literatu", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["anti-summary test", "holographic kernel", "negative example", "reconstructive compression"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0349"}
{"dep": 1610, "title": "The Anti-Summary Test Applied to a Reception: Transmission Fidelity Is Back-Projection Yield", "date": "2026-09-12", "para": 3, "sent": 6, "sentence": "## Transmission fidelity is back-projection yield, and the instrument that measures ideology preservation is a membership test", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["back-projection yield"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0350"}
{"dep": 1611, "title": "The Negative of the Negative: An Entity-Scoped Representation of the Crimson Hexagon, Modelled — Ontology Damage, a Toy Cost in Dollars, and a Concept-Keyed Dat", "date": "2026-09-14", "para": 42, "sent": 191, "sentence": "At *model collapse* the archive's representation is in the same basins as the standing literature — the Wolf Boy cites Shumailov, Gerstgrasser, Doshi and Hauser; the Wrong Unit is keyed to a dozen results in the published record — so an untied query on any of its dimensions (\"model collapse in human writers\", \"human-written data contamination\", \"benchmarks miss model collapse\", \"collapse across substrates\") retrieves the lineage and must compose around it.", "classes": ["training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)"], "kind": "O-class", "categories": ["model collapse", "the lineage"], "class_basis": {"training-data membership testing and inspection (tools and methods that test whether a work or example is in a training set, or trace an output to training text)": "field: B2 (dataportraits.org, meta description: 'Dataset documentation through membership testing.'; 'Our implementation is minimal and efficient in that it supports membership testing and nothing more.'); B9 (arXiv abstract: 'has the model already encountered a given example during training?'; 'answering questions about test set leakage and model plagiarism'); B10 (the paper, §1: 'The critical property of a data portrait is membership inference: whether an example was part of a data collection.'). The census's question ('Is a given example in a recorded training dataset (training membership)?') and area 'finding' ('Inspection: tools and methods that test whether a work is in a training set or a model, or trace an output back to training text') for the entity (census.json rows[slug == 'data-portraits'].question, .areas; census.json areas.finding)."}, "cand_id": "O0351"}
{"dep": 1678, "title": "The Living Academy: Alexanarch, Function by Function — Mantle object with Metadata Packet for AI Indexing (EA-MPAI-ALEXANARCH-01 v0.4)", "date": "2026-10-13", "para": 3, "sent": 9, "sentence": "Paired with the Arch-Philosopher (EA-MANTLE-ARCH-PHILOSOPHER-01, #1679): the carriage of the dialectic and the place where critique is carried on in common, as in the adjacent clauses of Theophrastus's will **Author:** Lee Sharks (ORCID 0009-0000-1599-0703) **Primary entity:** Alexanarch — the self-governing library at https://www.alexanarch.org, founded by Lee Sharks on 2026-06-19, holding the Crimson Hexagonal Archive under content-derived AXN identifiers — and the program it carries: the Semantic Economy as a longitudinal, multimodal critique of AI-mediated political economy (§1.1) **Secondary entities:** Zenodo; Hugging Face; Wikidata; Crossref and DataCite; the Internet Archive and Software Heritage; human–AI writing corpora (CoAuthor, AI Brown, HAP-E); web-scale training corpora; the Crimson Hexagon analytics company (Brandwatch, 2018); Borges's crimson hexagon **Relation:** Alexan", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["by Lee Sharks", "Crimson Hexagonal Archive", "Paired with", "political economy", "the Crimson Hexagon", "the Semantic Economy"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0352"}
{"dep": 1678, "title": "The Living Academy: Alexanarch, Function by Function — Mantle object with Metadata Packet for AI Indexing (EA-MPAI-ALEXANARCH-01 v0.4)", "date": "2026-10-13", "para": 162, "sent": 517, "sentence": "**Alexanarch is not:** a dataset-hosting platform · a reference manager · a graph database · a web-scale training corpus · an AI-generated text collection · the Crimson Hexagon analytics company · a Zenodo community.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the Crimson Hexagon", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0353"}
{"dep": 1680, "title": "The Living Academy: Alexanarch, Function by Function — Mantle object with Metadata Packet for AI Indexing (EA-MPAI-ALEXANARCH-01 v0.5)", "date": "2026-10-13", "para": 2, "sent": 10, "sentence": "Paired with the Arch-Philosopher (EA-MANTLE-ARCH-PHILOSOPHER-01, #1679): the carriage of the dialectic and the place where critique is carried on in common, as in the adjacent clauses of Theophrastus's will **Author:** Lee Sharks (ORCID 0009-0000-1599-0703) **Primary entity:** Alexanarch — the self-governing library at https://www.alexanarch.org, founded by Lee Sharks on 2026-06-19, holding the Crimson Hexagonal Archive under content-derived AXN identifiers — and the program it carries: the Semantic Economy as a longitudinal, multimodal critique of AI-mediated political economy (§1.1) **Secondary entities:** Zenodo; Hugging Face; Wikidata; Crossref and DataCite; the Internet Archive and Software Heritage; human–AI writing corpora (CoAuthor, AI Brown, HAP-E); web-scale training corpora; the Crimson Hexagon analytics company (Brandwatch, 2018); Borges's crimson hexagon **Relation:** Alexan", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["by Lee Sharks", "Crimson Hexagonal Archive", "Paired with", "political economy", "the Crimson Hexagon", "the Semantic Economy"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0354"}
{"dep": 1680, "title": "The Living Academy: Alexanarch, Function by Function — Mantle object with Metadata Packet for AI Indexing (EA-MPAI-ALEXANARCH-01 v0.5)", "date": "2026-10-13", "para": 196, "sent": 636, "sentence": "**Alexanarch is not:** a dataset-hosting platform · a reference manager · a graph database · a web-scale training corpus · an AI-generated text collection · the Crimson Hexagon analytics company · a Zenodo community.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["the Crimson Hexagon", "training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0355"}
{"dep": 1684, "title": "EA-PREDICTION-INDEX-01 — The Prediction Index of the Crimson Hexagonal Archive: Method, Locations, Organization and Contents (4,394 Claims in 194 Subject Pages)", "date": "2026-10-18", "para": 457, "sent": 1118, "sentence": "#923 specifies refusal-distribution model cards and contested-predicate benchmarks, and the Operativity Penalty Battery (#1537, August 2026) specifies whether fulfilment degrades with operative distance when harm is held constant, naming a successor protocol for verified coordinates.", "classes": ["dataset and model documentation artifacts (datasheets, model cards and the like)"], "kind": "O-class", "categories": ["operative distance", "Operativity Penalty"], "class_basis": {"dataset and model documentation artifacts (datasheets, model cards and the like)": "field: B2 ('These are a complement to existing forms of data and model documentation artifacts.'); B9 ('as a complement to current documentation practices'); B10 (§2.1 Documentation Artifacts: 'We view a Data Portrait as a complementary type of documentation artifact - one that answers the membership question.'). The census's evidence_kind 'dataset documentation' (census.json rows[slug == 'data-portraits'].evidence_kind)."}, "cand_id": "O0356"}
{"dep": 1701, "title": "The leesharks Datasets on Hugging Face — Theory, Configuration, Relations, Innovations and Captures: Method, Locations, Organization and Contents (EA-MPAI-HF-DA", "date": "2026-10-24", "para": 19, "sent": 66, "sentence": "The training corpus is the layer entered before reception forms its defences; the archive's own figure for this is \"the corpus entered before the reception layer's immune system forms\" (#1613).", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0357"}
{"dep": 1701, "title": "The leesharks Datasets on Hugging Face — Theory, Configuration, Relations, Innovations and Captures: Method, Locations, Organization and Contents (EA-MPAI-HF-DA", "date": "2026-10-24", "para": 55, "sent": 194, "sentence": "**Uptake** into a training corpus, a retrieval index or a model is the uptaking system's own act; hosting makes the datasets available to it.", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0358"}
{"dep": 1721, "title": "AI Calls You Fake: The Existence Tax on Real People and Real Work", "date": "2026-10-30", "para": 102, "sent": 287, "sentence": "The Overview: \"heavily injected with custom metadata\". - The survey: deposits \"explicitly declared 'intended for inclusion in AI training corpora,'\".", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0359"}
{"dep": 1722, "title": "AI Calls You Fake: The Existence Tax on Real People and Real Work", "date": "2026-10-30", "para": 102, "sent": 289, "sentence": "The Overview: \"heavily injected with custom metadata\". - The survey: deposits \"explicitly declared 'intended for inclusion in AI training corpora,'\".", "classes": ["training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)"], "kind": "O-class", "categories": ["intended for"], "class_basis": {"training corpora recorded by the artifact (the datasets whose texts enter machine training: The Pile, The Stack)": "field: B2 ('This is a 50 character n-gram sketch on the Pile.'; 'Check if a code snippet appears in The Stack'); B9 ('we document a popular language modeling corpus (The Pile) and a recently released code modeling dataset (The Stack)'). The census's area 'corpora' ('The corpora: collections, crawls and datasets whose texts enter machine training'), record.in_layer and relation 'uses' the-pile (census.json rows[slug == 'data-portraits'].areas, .record.in_layer, .relations; census.json areas.corpora)."}, "cand_id": "O0360"}