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  "quote": "The composition reaches the Initiative's current pages, which this record's fetcher could not render, and corrects the record's scale.",
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  "locus": "data-provenance-explorer-aio-20261029, q_kind",
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  "quote": "The Data Provenance Initiative named; Shayne Longpre and Sara Hooker on the Cohere card only; the Initiative's pages cited.",
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  "quote": "The Initiative calls its work \"public measurement infrastructure for AI data, model ecosystems, and real-world AI use.\"",
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  "claim": "practitioners trace and filter provenance of popular open fine-tuning collections back to sources, creators and licences",
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  "quote": "The founding audit (arXiv:2310.16787) found \"license omission of 70%+ and error rates of 50%+\" on dataset hosting sites.",
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  "file": "/home/claude/fleet/machinemediation-org/scripts/meta_aggregator/articles.py",
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  "claim": "Consent in Crisis: 14,000 web domains; 45% of C4 restricted by terms of service",
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  "quote": "Consent in Crisis (arXiv:2407.14933, NeurIPS 2024) read 14,000 web domains and found that \"a full 45% of C4 is now restricted\" by terms of service.",
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  "lineage": "consent findings: the decline of the AI data commons",
  "date": "2026-10-28 (corrected 2026-10-29)",
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  "quote": "It records provenance at the training layer, where the census's reception aggregators record it at the composition layer.",
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  "claim": "two ends of one chain: what a model was given, and what it credits when it answers",
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  "quote": "The two ends of one chain: what a model was given, and what it credits when it answers.",
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 {
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 {
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  "quote": "| L4: Provenance / Consent | Where did this come from | Data Provenance Initiative, DECORAIT (narrow) | Integrated provenance in training pipeline |",
  "kernel": false,
  "const": false,
  "lineage": "provenance has to survive the training pipeline",
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 },
 {
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  "claim": "the archive adapts the Initiative's provenance schema as training-pipeline provenance, implemented in the Crimson Hexagonal Archive",
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  "quote": "| Provenance schema | Data Provenance Initiative | Training-pipeline provenance | Implemented (in CHA) |",
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 },
 {
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  "quote": "Gebru et al.'s proposal for \"datasheets for datasets\" — standardized documentation of training data provenance — is a step toward the accounting the framework demands.",
  "kernel": false,
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  "lineage": "the accounting: whose labour produced the training data",
  "date": "2026-04-01",
  "title": "Semantic Infrastructure: From Tim Berners-Lee to the Semantic Economy — Bridging Technical and Political-Economic Frameworks",
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  "o_class": "training-data provenance and licensing audits (basis: field B7 Abstract; B3 home; B6 ¶2)"
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 {
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  "claim": "PER reaches parametric claims only indirectly; at the substrate level they need companion infrastructure, training-data provenance records among it",
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  "date": "2026-05-05",
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 {
  "id": "D725-01",
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  ],
  "locus": "Canonical Claim",
  "quote": "Existing provenance frameworks address the artifact (C2PA / Content Credentials) and the corpus (Data Provenance Initiative, EU AI Act transparency provisions, W3C PROV). They are not designed to address the survival of authorial lineage through AI synthesis.",
  "kernel": true,
  "const": true,
  "lineage": "the licensing dimension among the dimensions of provenance",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 451,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "D725-02",
  "dep": 725,
  "record": "deposit",
  "claim": "the Initiative's audit as #725 reports it: published in Nature Machine Intelligence 2024; 85% of licences request attribution, 30% share-alike; omission above 70%, errors above 50%",
  "kind": "fact",
  "modality": "attributed",
  "strata": [
   "D"
  ],
  "locus": "¶ 'The second dimension — training-corpus licensing'",
  "quote": "The Data Provenance Initiative (Longpre et al., Nature Machine Intelligence 2024) audited 1,800+ datasets, finding that 85% of licenses request attribution and 30% include share-alike clauses, with license omission rates above 70% and error rates above 50% on popular hosting sites.",
  "kernel": false,
  "const": false,
  "lineage": "audit findings: licence omission and misattribution",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 1953,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "O725-03",
  "dep": 725,
  "record": "deposit",
  "claim": "training-corpus licensing is the second dimension of provenance, with academic instrumentation and emerging legal architecture",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "O"
  ],
  "locus": "¶ 'The second dimension — training-corpus licensing'",
  "quote": "The second dimension — **training-corpus licensing** — has academic instrumentation and emerging legal architecture.",
  "kernel": false,
  "const": false,
  "lineage": "the licensing dimension among the dimensions of provenance",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 1836,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)",
  "o_class": "training-data provenance and licensing audits (basis: field B7 Abstract; B3 home; B6 ¶2)"
 },
 {
  "id": "D725-04",
  "dep": 725,
  "record": "deposit",
  "claim": "the Initiative audits whether datasets were licensed, not whether synthesized outputs preserve attribution to the human sources",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "D"
  ],
  "locus": "¶ 'Existing frameworks are not designed to detect this'",
  "quote": "The Data Provenance Initiative audits whether datasets were licensed, not whether synthesized outputs preserve attribution to the human sources whose labor the synthesis depended upon.",
  "kernel": true,
  "const": false,
  "lineage": "the limit: the input, not the output",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 3736,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "O725-05",
  "dep": 725,
  "record": "deposit",
  "claim": "the licensing dimension: its object the corpus, its question under what permissions data entered the system, its frameworks the Initiative among them",
  "kind": "definition",
  "modality": "stipulation",
  "strata": [
   "O"
  ],
  "locus": "Central Invariant, table row 'Licensing'",
  "quote": "| **Licensing** | The corpus | Under what permissions did this data enter this system? | Data Provenance Initiative, EU AI Act, W3C PROV |",
  "kernel": false,
  "const": false,
  "lineage": "the licensing dimension among the dimensions of provenance",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 4841,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "O725-06",
  "dep": 725,
  "record": "deposit",
  "claim": "the dimensions are independent: licensing does not settle semantic provenance, nor the reverse",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "O"
  ],
  "locus": "Central Invariant, ¶ after the table",
  "quote": "The dimensions are independent: a C2PA-signed image can carry meaning whose lineage has been eroded; a licensed dataset can produce outputs with excellent semantic provenance if the synthesis preserves attribution; an unlicensed dataset can produce outputs that nonetheless cite their sources clearly. Solving any one dimension does not solve the others.",
  "kernel": false,
  "const": false,
  "lineage": "the licensing dimension among the dimensions of provenance",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 5365,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)",
  "o_class": "training-data provenance and licensing audits (basis: field B7 Abstract; B3 home; B6 ¶2)"
 },
 {
  "id": "O725-07",
  "dep": 725,
  "record": "deposit",
  "claim": "semantic provenance defined: the dimension extending provenance to the lifecycle of the meaning content carries",
  "kind": "definition",
  "modality": "stipulation",
  "strata": [
   "O"
  ],
  "locus": "¶ 'Semantic provenance names the dimension'",
  "quote": "**Semantic provenance** names the dimension that completes the C2PA ambition of trust in digital content by extending provenance from the moment of creation to the lifecycle of the meaning the content carries.",
  "kernel": false,
  "const": false,
  "lineage": "the licensing dimension among the dimensions of provenance",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 4058,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "D725-08",
  "dep": 725,
  "record": "deposit",
  "claim": "licensing audits operate on the input, not the output; the Initiative's 70%+ omission establishes the licensing dimension's urgency; semantic provenance addresses the dimension that follows",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "D",
   "O"
  ],
  "locus": "§3.2 Misreading: provenance as licensing-only, Correction",
  "quote": "Licensing audits operate on the *input* to AI systems. They do not address the *output*. A model trained on properly licensed scholarship can still produce outputs that erase the scholarship's lineage. Licensing provenance and semantic provenance are different problems requiring different instruments. The DPI's documentation of 70%+ license-omission rates establishes the licensing dimension's urgency; semantic provenance addresses the dimension that follows.",
  "kernel": true,
  "const": false,
  "lineage": "the limit: the input, not the output",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 19942,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "D725-09",
  "dep": 725,
  "record": "deposit",
  "claim": "the Initiative tells whether the dataset was licensed; no such instrument tells what happened to the meaning between the dataset and the answer",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "D"
  ],
  "locus": "closing ¶ ('C2PA tells you …')",
  "quote": "The Data Provenance Initiative tells you whether the dataset was licensed. EU AI Act transparency provisions tell you whether the content was AI-generated. None of these instruments was designed to tell you what happened to the meaning between the camera and the screen, between the dataset and the answer, between the source and the synthesis.",
  "kernel": false,
  "const": false,
  "lineage": "the limit: the input, not the output",
  "date": "2026-05-08",
  "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance",
  "creator": "Lee Sharks",
  "file": "data/texts/AXN-0273-text.md",
  "offset": 39000,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "D1003-01",
  "dep": 1003,
  "record": "deposit",
  "claim": "provenance and attribution studies, the Initiative among them, have focused on attribution loss in AI outputs",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "D"
  ],
  "locus": "¶ 'Provenance and attribution studies'",
  "quote": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs.",
  "kernel": false,
  "const": false,
  "lineage": "the limit: the input, not the output",
  "date": "2026-05-21",
  "title": "AI-Native Intellectual Biography: Genre, Provenance, and the Archive That Outpaced Its Author Description (as recorded by DataCite)",
  "creator": "Sharks, Lee",
  "file": "data/texts/AXN-03F7-text.md",
  "offset": 13986,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)",
  "note": "#121 and #1194 carry the same sentence (one work, one lineage; #1003 the earliest dated, 2026-05-21)"
 },
 {
  "id": "D1714-01",
  "dep": 1714,
  "record": "deposit",
  "claim": "what the audit literature shows is lost before training: the Initiative found licence information missing for more than 70% and mislabelled for more than half",
  "kind": "fact",
  "modality": "attributed",
  "strata": [
   "D"
  ],
  "locus": "§9.2 s3",
  "quote": "The audit literature shows how much is lost before training even begins: the Data Provenance Initiative found license information missing for more than 70% of widely used datasets on the major hosting sites and mislabeled for more than half (Longpre et al. 2023).",
  "kernel": false,
  "const": false,
  "lineage": "provenance has to survive the training pipeline",
  "date": "2026-10-27",
  "title": "Meaning Feudalism at the Discovery Layer: Shepherds, Raw Research Data, and the Allocation of Semantic Capital in OpenAI's Mathematics and Science Releases, October 2026 (EA-SEM-FEUDALISM-03 v1.0)",
  "creator": "Sharks, Lee",
  "file": "data/texts/AXN-0A7F-text.md",
  "offset": 50458,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "D1714-02",
  "dep": 1714,
  "record": "deposit",
  "claim": "provenance has to survive the training pipeline; the slushing is a property of how the pipeline is built",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "D"
  ],
  "locus": "§9.2 s1–2",
  "quote": "Provenance has to survive the training pipeline. The pipeline is a built thing, and the slushing is a property of how it is built.",
  "kernel": true,
  "const": false,
  "lineage": "provenance has to survive the training pipeline",
  "date": "2026-10-27",
  "title": "Meaning Feudalism at the Discovery Layer: Shepherds, Raw Research Data, and the Allocation of Semantic Capital in OpenAI's Mathematics and Science Releases, October 2026 (EA-SEM-FEUDALISM-03 v1.0)",
  "creator": "Sharks, Lee",
  "file": "data/texts/AXN-0A7F-text.md",
  "offset": 50327,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 },
 {
  "id": "D1714-03",
  "dep": 1714,
  "record": "deposit",
  "claim": "neither audit nor influence tracing recovers authorship; what survives depends on what is recorded; sources can be recorded at ingestion and kept with the training set",
  "kind": "interpretation",
  "modality": "interpretation",
  "strata": [
   "D"
  ],
  "locus": "§9.2 s5–6",
  "quote": "Neither recovers authorship, and both show that what survives depends on what is recorded and computed. Sources can be recorded at ingestion, the record kept with the training set, and a result released with the dependencies its producer can report",
  "kernel": false,
  "const": false,
  "lineage": "provenance has to survive the training pipeline",
  "date": "2026-10-27",
  "title": "Meaning Feudalism at the Discovery Layer: Shepherds, Raw Research Data, and the Allocation of Semantic Capital in OpenAI's Mathematics and Science Releases, October 2026 (EA-SEM-FEUDALISM-03 v1.0)",
  "creator": "Sharks, Lee",
  "file": "data/texts/AXN-0A7F-text.md",
  "offset": 50906,
  "quote_ok": true,
  "quote_check": "verbatim in the deposit text (whitespace-normalised)"
 }
]