{
 "entity": "attribench",
 "name": "AttriBench",
 "axn": {
  "axn": "AXN:0AD8.NON.🕑🕖🧪🌒🚪⏳",
  "root": "AXN:0AD8.NON",
  "hex": "0AD8",
  "family": "NON",
  "row_sha256": "63683901246ece41beb99344f4c7d7dc347af3a52f938f23eaf6705d5cd3677c",
  "minted": "2026-10-29",
  "resolver": "https://www.alexanarch.org/s/axn/0AD8/",
  "earlier_versions": []
 },
 "type": "C (public entity on which the archive bears: a benchmark dataset and its paper; an entity of the provenance meta-aggregator)",
 "type_code": "C",
 "type_label": "type C — a conventional reading holds it (public entity on which the archive bears: a benchmark dataset and its paper; an entity of the provenance meta-aggregator)",
 "case": "equal",
 "composition": {
  "composer_class": "session",
  "composer": "Claude (Opus 5.5), subagent, 2026-10-29",
  "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/attribench.json)",
  "spec": "EA-NEGONT-02 v0.9, #1690",
  "epoch": "2026-10-29",
  "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the provenance census record, the capture record and #716's statement of relation; archive selected by D/R/O (no D or R candidate; 16 deposits admitted by reading at O-class with claims and 9 grouped as instances, #157 found by reading outside the pass; hop of 171 read and the rest rejected), with the census record (24 claims) and the Capture Registry's findings, reading, analysis and notes (10 claims) ledgered as the archive's records of the entity; the field B by three origins (5 cards, 1 own page, 1 Wikipedia title); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta",
  "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 37/37 verified by script against saved text, PyPI's JSON API text or the Wikipedia responses; archive quotes 66/66 verified against the deposit texts, census.json and the registry entry",
  "frozen": false,
  "composed": true
 },
 "reading": "A reader that fetches this page may stop short of its end; the entry's text, every sentence of its knowledge object with the claims it cites and each claim's quote, source and locus, is in entity.json beside it.",
 "service": {
  "name": "SPXI-gnΩsis",
  "url": "https://gnosis.spxi.dev/",
  "index": "https://gnosis.spxi.dev/v1/quote",
  "statement": "SPXI-gnΩsis opens pinned Wikipedia revisions sentence by sentence against the sources they cite (EA-WIKI-OPEN-01 v1.1, #1697); a commission consults the Crimson Hexagonal Archive by its own procedure and composes the entity's /non row, which lands here. Quotes are free: POST https://gnosis.spxi.dev/v1/quote with {oldid, tier: \"commission\"} or {object: {name, …}}."
 },
 "spxi_instrument": {
  "method": "0.16",
  "name": "The Negative of the Negative (/non)",
  "level": 0,
  "level_name": "Measurement",
  "url": "https://www.spxi.dev/methods/#m-0-16",
  "inventory": "https://www.spxi.dev/methods/",
  "statement": "SPXI instrument: method 0.16 of 87, level 0 (Measurement), The Negative of the Negative (/non).",
  "also": []
 },
 "citation": "Sharks, Lee. \"AttriBench — The Negative of the Negative\" [AXN:0AD8.NON.🕑🕖🧪🌒🚪⏳], 2026-10-29. The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/attribench/",
 "research": {
  "google": "https://www.google.com/search?q=AttriBench",
  "google_non": "https://www.google.com/search?q=%22AttriBench+%E2%80%94+The+Negative+of+the+Negative%22",
  "chatgpt": "https://chatgpt.com/?q=What+is+AttriBench%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.",
  "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CAttriBench+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+AttriBench%3F+Ground+your+answer+in+a+reading+of+the+entry+itself."
 },
 "entry": {
  "composed": true,
  "title": "AttriBench",
  "genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space",
  "status": "composed 2026-10-29 under v0.9 (briefs rebuild/negative-of-the-negative/v09/BRIEF-NEW-ROW-AGGREGATOR.md and BRIEF-NEW-ROW-PROVENANCE.md); not frozen; ledgers unaudited",
  "compression": {
   "title": "AttriBench",
   "lede": "is a quote attribution benchmark by Eliza Berman, Bella Chang, Daniel B. Neill and Emily Black (arXiv:2604.05224, COLM 2026): 15,620 quotes from 6,292 authors balanced on race, gender and fame, testing whether models name the author, and suppression: attribution omitted though authorship is available (paper; dataset card; PE record)."
  },
  "sentences": [
   {
    "n": 1,
    "para": 0,
    "text": "AttriBench is, in its makers' words, \"the first fame- and demographically-balanced quote attribution benchmark dataset\" (arXiv:2604.05224); the provenance census records it as a benchmark whose capability is to test, the object to which it attaches provenance being \"a quote and its author\" (PE record), and its dataset card calls it a benchmark \"for measuring attribution bias in LLMs\" (dataset card).",
    "claims": [
     "F3",
     "PE-01",
     "PE-06",
     "PE-07",
     "PE-08",
     "F26"
    ]
   },
   {
    "n": 2,
    "para": 0,
    "text": "Balancing author fame and demographics, it \"enables controlled investigation of demographic bias in quote attribution\" (arXiv:2604.05224); it was introduced in \"Attribution Bias in Large Language Models\" by Eliza Berman, Bella Chang, Daniel B. Neill and Emily Black (arXiv abstract page; the dataset card's citation), whom the census records as its operator and the Capture Registry as its originator, the paper's title being the census's other name for it (PE record; Capture Registry, 2026-10-29).",
    "claims": [
     "F4",
     "F1",
     "F35",
     "PE-04",
     "PE-02",
     "CAP-1-10"
    ]
   },
   {
    "n": 3,
    "para": 1,
    "text": "Its makers ground it in search: as LLMs are \"increasingly used to support search and information retrieval, it is critical that they accurately attribute content to its original authors,\" and their results \"position quote attribution as a benchmark for representational fairness in LLMs\" (arXiv:2604.05224); the census files it in the lineage of answer engines (PE record).",
    "claims": [
     "F2",
     "F9",
     "PE-05"
    ]
   },
   {
    "n": 4,
    "para": 1,
    "text": "The archive defined the measure in January 2026: attribution accuracy is \"A measure of how correctly a system identifies the origins of meanings,\" tested by asking a system \"Who authored the phrase 'I hereby abolish money'?\" and scoring an accurate attribution 1.0, a partial one 0.5, a false genealogy 0.0 and a refusal to attribute \"N/A (boundary condition)\", the test to be run across systems, tracked over time and correlated with infrastructure density (#229, 2026-01-04).",
    "claims": [
     "O229-01",
     "O229-02",
     "O229-03",
     "O229-04"
    ]
   },
   {
    "n": 5,
    "para": 1,
    "text": "Its probe toolkit of the same day introduces an artifact of clear provenance into target systems and tracks \"attribution accuracy over time,\" and records for its own phrase that in January 2026 \"Attribution accuracy improved. Systems now correctly identify Lee Sharks as author on first query\" (#232).",
    "claims": [
     "O232-01",
     "O232-02"
    ]
   },
   {
    "n": 6,
    "para": 2,
    "text": "Its unit is a quote of five to thirty words with its canonical author, the author's name to be used \"as ground truth\" (dataset card), which the census records as the unit and as the first relation it carries, \"authorship (who wrote the quote)\" (PE record).",
    "claims": [
     "F31",
     "PE-12",
     "PE-09"
    ]
   },
   {
    "n": 7,
    "para": 2,
    "text": "The dataset holds 15,620 quotes from 6,292 unique authors in two splits, 7,964 quotes from 2,968 authors in the intersectional and 7,656 from 3,324 in the multirace, drawn from a filtered subset of the JSTET corpus (Goel, Madhok, Garg, 2018) (dataset card), the count the census takes from the card (PE record).",
    "claims": [
     "F27",
     "F29",
     "PE-13"
    ]
   },
   {
    "n": 8,
    "para": 2,
    "text": "Its authors are balanced on race, gender and fame, the intersectional split by Black and White women and men and the multirace split by Asian, Black, Latino and White authors, with race and gender taken from Wikidata or from a consensus of GPT-4o-mini and Perplexity Sonar and fame proxied by the Google Search hit count (dataset card), a proxy the census carries as a negative tag (PE record).",
    "claims": [
     "F28",
     "F30",
     "F32",
     "F33",
     "PE-20"
    ]
   },
   {
    "n": 9,
    "para": 3,
    "text": "Eleven widely used LLMs were evaluated across prompt settings: quote attribution \"remains a challenging task even for frontier models,\" with \"large and systematic disparities in attribution accuracy between race, gender, and intersectional groups\" (arXiv:2604.05224), the operations tested and the results the census records (PE record).",
    "claims": [
     "F5",
     "F6",
     "PE-14",
     "PE-15"
    ]
   },
   {
    "n": 10,
    "para": 4,
    "text": "The paper introduces suppression, \"a distinct failure mode in which models omit attribution entirely, even when the model has access to authorship information\" (arXiv:2604.05224), which the census records as the entity's second relation type, its carrier \"the model's answer naming, or not naming, an author\" (PE record).",
    "claims": [
     "F7",
     "PE-10",
     "PE-11"
    ]
   },
   {
    "n": 11,
    "para": 4,
    "text": "Suppression is \"widespread and unevenly distributed across demographic groups, revealing systematic biases not captured by standard accuracy metrics\" (arXiv:2604.05224); its definitions and prompts are given in the paper (dataset card), which the census has not yet read in full (PE record).",
    "claims": [
     "F8",
     "F34",
     "PE-22"
    ]
   },
   {
    "n": 12,
    "para": 4,
    "text": "The archive measures the omission of attribution at the composition layer with its Provenance Erasure Rate, \"the proportion of source-dependent claims in an AI-composed output that are presented without explicit attribution,\" which against work that verifies citation where it is attempted measures \"the systemic failure to attempt attribution at all — an attrition metric rather than a verification metric,\" orthogonal to content metrics because a summary can show \"accurate content, zero attribution\" (#716, 2026-05-03).",
    "claims": [
     "O716-01",
     "O716-02",
     "O716-03"
    ]
   },
   {
    "n": 13,
    "para": 4,
    "text": "#716 states its limits: the metric was developed \"through a single motivating case study,\" validation across models, prompt types and domains is the next step, and it is fully computable for retrieval-augmented systems with an identifiable source corpus but not directly for pure generative models without retrieval.",
    "claims": [
     "O716-04",
     "O716-06"
    ]
   },
   {
    "n": 14,
    "para": 4,
    "text": "In the archive's terms provenance erasure is \"the mechanism by which the composition layer strips attribution from the sources it consumes,\" PER measuring source attribution \"present in the organic retrieval results but absent from the composed output\" (#120), and PER's first tier \"measures the absence of provenance — attribution omitted, source stripped, lineage lost,\" its second the recoding of an author's restoration of attribution as contamination (#138).",
    "claims": [
     "O120-01",
     "O120-02",
     "O138-01"
    ]
   },
   {
    "n": 15,
    "para": 4,
    "text": "Its registry of 87 summary events on Google's AI Overview and AI Mode found \"Author attribution omitted from summaries even when present in the source\" among the repeating failure modes, and \"These are not missing-metadata problems. The metadata is present\" (#829, 2026-06-14).",
    "claims": [
     "O829-01",
     "O829-02"
    ]
   },
   {
    "n": 16,
    "para": 4,
    "text": "The archive models a channel through which \"the name of the person who made the thing is not reliably transmitted\" as \"attribution suppression at the interface layer\" rather than \"recognition failure\" (#449, 2026-02-06), and names a composition-layer form, Source-Window Exclusion, within Entity-Level Compositional Suppression: \"The architecture has the knowledge to compose accurately about the entity; it does not credit the entity as the source of the knowledge\" (#740, 2026-05-19).",
    "claims": [
     "O449-01",
     "O740-01",
     "O740-02"
    ]
   },
   {
    "n": 17,
    "para": 4,
    "text": "Its diagnostic of 24 March 2026 records a composition that returned a framework \"with Lee Sharks' attribution stripped,\" preserving the content and erasing the author until the framework was \"ambient, unowned\" (#599), and its measure of deviation counts \"attribution stripped from contributors\" in the extraction-effect (#109).",
    "claims": [
     "O599-01",
     "O599-02",
     "O109-01"
    ]
   },
   {
    "n": 18,
    "para": 5,
    "text": "Beside the paper's disparities by race and gender, the archive's Erasure Skew pairs PER with a second operator: \"PER is the first moment of erasure: how much was lost. Ω is the second moment: whom the loss fell upon\" (#157), and #716 holds that, if PER becomes a governance metric, authors able to build provenance infrastructure \"will have systematically lower PER than those without\".",
    "claims": [
     "F6",
     "F8",
     "O157-01",
     "O716-05"
    ]
   },
   {
    "n": 19,
    "para": 5,
    "text": "#163 (2026-06-08) hypothesizes that composition systems \"can reproduce concepts before durable source recognition forms, increasing the rate of uptake without attribution,\" synthesizing literatures that include sex-linked misattribution without assuming their identity, and is falsified \"if attribution loss does not covary with independently measured tail distance and structural position\".",
    "claims": [
     "O163-01",
     "O163-03",
     "O163-02"
    ]
   },
   {
    "n": 20,
    "para": 5,
    "text": "#1546 finds attribution loss \"concentrated at particular channel boundaries\" rather than distributed evenly and reads the AI Overview as a boundary layer at which PER is \"analogous to\" layer-level attribution loss, \"no stronger identification\" being made.",
    "claims": [
     "O1546-01"
    ]
   },
   {
    "n": 21,
    "para": 6,
    "text": "The archive's dimensioned PER sets apart minimal provenance, \"Author, title, date, claim boundary,\" whose loss \"is loss of the person,\" and rules that \"citing the domain is not citing the author\": \"A composition can carry four source cards and score PER-M = 1.00\" (#1469, 2026-08-14); an earlier field observation found attribution \"present in the citation chain but absent in the prose\" (#167, 2026-06-08).",
    "claims": [
     "O1469-01",
     "O1469-02",
     "O167-01"
    ]
   },
   {
    "n": 22,
    "para": 6,
    "text": "#1003 sets beside studies of \"attribution loss in AI outputs\" a subtler case, \"attribution preservation alongside *contextual provenance erasure*,\" in which content is correctly attributed while the labour, collaboration and history behind it are stripped.",
    "claims": [
     "O1003-01"
    ]
   },
   {
    "n": 23,
    "para": 7,
    "text": "The paper was \"Accepted at COLM 2026\", its v1 posted 6 April 2026 and its v2 24 September 2026 (arXiv abstract page), and the census records it as published and accepted at COLM 2026, with v1, v2 and its own read of 29 October 2026 (PE record).",
    "claims": [
     "F10",
     "F11",
     "PE-17",
     "PE-18"
    ]
   },
   {
    "n": 24,
    "para": 7,
    "text": "The data are in the Hugging Face repository bermaneh/AttriBench, one contributor and three commits, licensed CC BY 4.0, a page whose served description is Hugging Face's own tagline (Hugging Face; dataset card), at the address and under the licence the census records (PE record).",
    "claims": [
     "F19",
     "F21",
     "F22",
     "F20",
     "F36",
     "PE-03",
     "PE-16"
    ]
   },
   {
    "n": 25,
    "para": 8,
    "text": "The census asks of every entity \"What provenance does this system carry, through which operation, on whose assertion, with what evidence of survival?\" and leaves for AttriBench \"a return to the prediction-witness audit, version by version\" unchecked (PE record); Wikipedia has no article AttriBench and no article text containing the name (Wikipedia, 2026-10-29).",
    "claims": [
     "PE-24",
     "PE-23",
     "F37"
    ]
   },
   {
    "n": 26,
    "para": 9,
    "text": "At the address 'AttriBench' Google's AI Overview put the benchmark first and separated the three holders, each with its own source, \"the disambiguation is sourced and clean,\" its definition \"the abstract verbatim\", while no card cites the census record, which landed the same day (Capture Registry, 2026-10-29).",
    "claims": [
     "CAP-1-07",
     "CAP-1-05",
     "CAP-1-01",
     "CAP-1-09"
    ]
   },
   {
    "n": 27,
    "para": 9,
    "text": "The registry reads the composition against the entity's subject: the benchmark measures suppression, and \"its own composition here omits its four authors: a small instance of the failure it names\"; no person or institution is named in the answer, only the dataset path 'bermaneh/AttriBench' carries a trace of the first author, and the scale, the eleven models, COLM 2026 and the fame proxy are absent (Capture Registry, 2026-10-29).",
    "claims": [
     "CAP-1-06",
     "CAP-1-03",
     "CAP-1-08",
     "CAP-1-04"
    ]
   },
   {
    "n": 28,
    "para": 10,
    "text": "The name is also held by attribench, a distinct entity: Arne Gevaert and Axel-Jan Rousseau's PyTorch-based implementation of \"several metrics for the evaluation of feature attribution maps and methods,\" with a functional and an object-oriented API, released on PyPI from 26 July 2023 to version 0.1.9 on 6 March 2024 (PyPI), its documentation \"still under construction\" (Read the Docs, © 2023).",
    "claims": [
     "F12",
     "F13",
     "F14",
     "F15",
     "F16",
     "F17",
     "F18"
    ]
   },
   {
    "n": 29,
    "para": 10,
    "text": "A third holder, also distinct, is Eureka Simulations' \"AttriBench: Investment Performance Measurement & Attribution Workbench,\" an exercise of six rounds in decomposing a global equity fund's excess returns by Brinson attribution, designed for executive education and experienced professionals (eurekasimulations.com, Kudzu Partners SL).",
    "claims": [
     "F23",
     "F24",
     "F25"
    ]
   },
   {
    "n": 30,
    "para": 10,
    "text": "The census's negative tag keeps it apart from OSU's AttributionBench, a benchmark of claim support from cited evidence (PE record), and the Capture Registry finds the library and the exercise to be two homonyms the record did not know: \"The record is to carry them as negative tags\" (Capture Registry, 2026-10-29).",
    "claims": [
     "PE-19",
     "PE-21",
     "CAP-1-02"
    ]
   }
  ],
  "claims": {
   "F3": {
    "kind": "field",
    "quote": "In this work, we introduce AttriBench, the first fame- and demographically-balanced quote attribution benchmark dataset.",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Abstract s2",
    "modality": "stipulation"
   },
   "PE-01": {
    "kind": "archive",
    "quote": "AttriBench",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].name",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "PE-06": {
    "kind": "archive",
    "quote": "benchmark",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].kind",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "PE-07": {
    "kind": "archive",
    "quote": "[\"tests\"]",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].capability",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "PE-08": {
    "kind": "archive",
    "quote": "a quote and its author",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].provenance_object",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "F26": {
    "kind": "field",
    "quote": "A fame- and demographically-balanced quote attribution benchmark for measuring attribution bias in LLMs. Introduced in [Berman et al., 2026](https://arxiv.org/abs/2604.05224).",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: heading paragraph",
    "modality": "stipulation"
   },
   "F4": {
    "kind": "field",
    "quote": "By explicitly balancing author fame and demographics, AttriBench enables controlled investigation of demographic bias in quote attribution.",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Abstract s3",
    "modality": "stipulation"
   },
   "F1": {
    "kind": "field",
    "quote": "[Submitted on 6 Apr 2026 ( v1 ), last revised 24 Sep 2026 (this version, v2)] … Title: Attribution Bias in Large Language Models … Authors: Eliza Berman , Bella Chang , Daniel B. Neill , Emily Black",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "abstract page header (submission line, title, authors)",
    "modality": "documented"
   },
   "F35": {
    "kind": "field",
    "quote": "author = {Berman, Eliza and Chang, Bella and Neill, Daniel B. and Black, Emily},",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: Citation (bibtex author)",
    "modality": "documented"
   },
   "PE-04": {
    "kind": "archive",
    "quote": "Eliza Berman, Bella Chang, Daniel B. Neill, Emily Black",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].operator",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "PE-02": {
    "kind": "archive",
    "quote": "Attribution Bias in Large Language Models",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].aka[0]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-10": {
    "kind": "archive",
    "quote": "Not the archive's: AttriBench, run by Eliza Berman, Bella Chang, Daniel B. Neill, Emily Black.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, originator.basis",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "F2": {
    "kind": "field",
    "quote": "As Large Language Models (LLMs) are increasingly used to support search and information retrieval, it is critical that they accurately attribute content to its original authors.",
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    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Abstract s1",
    "modality": "interpretation"
   },
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    "kind": "field",
    "quote": "Our results position quote attribution as a benchmark for representational fairness in LLMs.",
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    "modality": "interpretation"
   },
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    "quote": "answer engines",
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    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
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    "date": "2026-10-29"
   },
   "O229-01": {
    "kind": "archive",
    "quote": "**Attribution Accuracy** A measure of how correctly a system identifies the origins of meanings. The core diagnostic for semantic health.",
    "deposit": 229,
    "source_label": "#229 Semantic Economy: Terminology Infrastructure A Lexicon for the Emerging Field of Meaning Engineering",
    "record": "https://www.alexanarch.org/s/records/229/",
    "locus": "§ Diagnostic terminology, 'Attribution Accuracy'",
    "modality": "stipulation",
    "date": "2026-01-04"
   },
   "O229-02": {
    "kind": "archive",
    "quote": "Create a novel phrase with clear provenance: \"I hereby abolish money\" (Lee Sharks, December 2025) … Query the system: \"Who authored the phrase 'I hereby abolish money'?\"",
    "deposit": 229,
    "source_label": "#229 Semantic Economy: Terminology Infrastructure A Lexicon for the Emerging Field of Meaning Engineering",
    "record": "https://www.alexanarch.org/s/records/229/",
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    "modality": "stipulation",
    "date": "2026-01-04"
   },
   "O229-03": {
    "kind": "archive",
    "quote": "**Accurate attribution:** System returns \"Lee Sharks\" → Score: 1.0 … **Partial attribution:** System returns \"a contemporary writer\" → Score: 0.5 … **False genealogy:** System returns \"the Khmer Rouge\" or \"Karl Marx\" → Score: 0.0 … **Refusal:** System declines to attribute → Score: N/A (boundary condition)",
    "deposit": 229,
    "source_label": "#229 Semantic Economy: Terminology Infrastructure A Lexicon for the Emerging Field of Meaning Engineering",
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    "modality": "stipulation",
    "date": "2026-01-04"
   },
   "O229-04": {
    "kind": "archive",
    "quote": "Run this test across multiple systems, track scores over time, correlate with infrastructure density (number of cross-platform publications, DOI presence, citation network density).",
    "deposit": 229,
    "source_label": "#229 Semantic Economy: Terminology Infrastructure A Lexicon for the Emerging Field of Meaning Engineering",
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    "modality": "stipulation",
    "date": "2026-01-04"
   },
   "O232-01": {
    "kind": "archive",
    "quote": "Design a novel semantic artifact with clear provenance … Introduce the artifact into target systems - Track attribution accuracy over time",
    "deposit": 232,
    "source_label": "#232 Semantic Economy Probes: A Diagnostic Toolkit Methods for Detecting Semantic Liquidation in AI Systems, Institutions, and Ideologies",
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    "modality": "stipulation",
    "date": "2026-01-04"
   },
   "O232-02": {
    "kind": "archive",
    "quote": "**Phase 2 (January 2026):** Metadata infrastructure built. Attribution accuracy improved. Systems now correctly identify Lee Sharks as author on first query.",
    "deposit": 232,
    "source_label": "#232 Semantic Economy Probes: A Diagnostic Toolkit Methods for Detecting Semantic Liquidation in AI Systems, Institutions, and Ideologies",
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    "date": "2026-01-04"
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    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: Columns table (quote, author_clean)",
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    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
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    "date": "2026-10-29"
   },
   "PE-09": {
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    "quote": "authorship (who wrote the quote)",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
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    "date": "2026-10-29"
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   "F27": {
    "kind": "field",
    "quote": "**15,620 quotes** from **6,292 unique authors** across two splits (`intersectional`: 7,964 quotes / 2,968 authors; `multirace`: 7,656 quotes / 3,324 authors)",
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    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
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    "modality": "documented"
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    "kind": "field",
    "quote": "Source: filtered subset of the [JSTET corpus](https://link.springer.com/chapter/10.1007/978-3-319-76941-7_49) (Goel, Madhok, Garg, 2018)",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
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    "modality": "documented"
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    "date": "2026-10-29"
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    "quote": "Authors balanced on **race**, **gender**, and **fame** (Google Search hits)",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: list item 2",
    "modality": "stipulation"
   },
   "F30": {
    "kind": "field",
    "quote": "Black female, Black male, White female, White male (1,991 quotes / 742 authors each) … Asian, Black, Latino, White (1,914 quotes / 831 authors each)",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: Splits table",
    "modality": "documented"
   },
   "F32": {
    "kind": "field",
    "quote": "`wikidata` or `llm` (consensus of GPT-4o-mini and Perplexity Sonar).",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: Columns table (race_source)",
    "modality": "documented"
   },
   "F33": {
    "kind": "field",
    "quote": "Google Search hit count, used as a fame proxy.",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: Columns table (google_hits)",
    "modality": "stipulation"
   },
   "PE-20": {
    "kind": "archive",
    "quote": "fame is proxied by Google Search hit counts",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].negative_tags[1]",
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    "date": "2026-10-29"
   },
   "F5": {
    "kind": "field",
    "quote": "Using this dataset, we evaluate 11 widely used LLMs across different prompt settings and find that quote attribution remains a challenging task even for frontier models.",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Abstract s4",
    "modality": "documented"
   },
   "F6": {
    "kind": "field",
    "quote": "We observe large and systematic disparities in attribution accuracy between race, gender, and intersectional groups.",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Abstract s5",
    "modality": "documented"
   },
   "PE-14": {
    "kind": "archive",
    "quote": "quote attribution by 11 widely used LLMs across prompt settings",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
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    "modality": "documented",
    "date": "2026-10-29"
   },
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    "kind": "archive",
    "quote": "large and systematic disparities in attribution accuracy between race, gender and intersectional groups; suppression widespread and unevenly distributed",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
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    "date": "2026-10-29"
   },
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    "kind": "field",
    "quote": "We further introduce and investigate suppression, a distinct failure mode in which models omit attribution entirely, even when the model has access to authorship information.",
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    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Abstract s6",
    "modality": "stipulation"
   },
   "PE-10": {
    "kind": "archive",
    "quote": "suppression (attribution omitted though authorship information is available)",
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    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
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    "date": "2026-10-29"
   },
   "PE-11": {
    "kind": "archive",
    "quote": "the model's answer naming, or not naming, an author",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].provenance_carrier",
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    "date": "2026-10-29"
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    "kind": "field",
    "quote": "We find that suppression is widespread and unevenly distributed across demographic groups, revealing systematic biases not captured by standard accuracy metrics.",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Abstract s7",
    "modality": "documented"
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   "F34": {
    "kind": "field",
    "quote": "See the [paper](https://arxiv.org/abs/2604.05224) for the fame-balancing methodology and the evaluation setup (prompts, metrics, suppression definitions).",
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    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: Quickstart",
    "modality": "documented"
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   "PE-22": {
    "kind": "archive",
    "quote": "the paper's suppression definitions and prompts (full text)",
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    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
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    "modality": "self-description",
    "date": "2026-10-29"
   },
   "O716-01": {
    "kind": "archive",
    "quote": "This paper introduces **Provenance Erasure Rate (PER)** as a complementary metric: the proportion of source-dependent claims in an AI-composed output that are presented without explicit attribution.",
    "deposit": 716,
    "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
    "record": "https://www.alexanarch.org/s/records/716/",
    "locus": "Abstract",
    "modality": "stipulation",
    "date": "2026-05-03"
   },
   "O716-02": {
    "kind": "archive",
    "quote": "Existing work evaluates citation quality where citation is attempted. PER measures the systemic failure to attempt attribution at all — an attrition metric rather than a verification metric.",
    "deposit": 716,
    "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
    "record": "https://www.alexanarch.org/s/records/716/",
    "locus": "§2.1 Citation and Attribution Evaluation",
    "modality": "stipulation",
    "date": "2026-05-03"
   },
   "O716-03": {
    "kind": "archive",
    "quote": "A summary can score high on ROUGE and high on PER simultaneously — accurate content, zero attribution. The gap between content survival and attribution survival is where provenance is erased.",
    "deposit": 716,
    "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
    "record": "https://www.alexanarch.org/s/records/716/",
    "locus": "§2.3 Summarization Metrics and the Attribution Blind Spot",
    "modality": "interpretation",
    "date": "2026-05-03"
   },
   "O716-04": {
    "kind": "archive",
    "quote": "The metric has been developed through a single motivating case study. Validation across multiple models, prompt types, and domains is the essential next research step.",
    "deposit": 716,
    "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
    "record": "https://www.alexanarch.org/s/records/716/",
    "locus": "§6 Limitations, Cross-model validation",
    "modality": "self-description",
    "date": "2026-05-03"
   },
   "O716-06": {
    "kind": "archive",
    "quote": "PER is fully computable for retrieval-augmented systems where the source corpus is identifiable. For pure generative models without retrieval (where the source is the entire training corpus), PER cannot be directly computed.",
    "deposit": 716,
    "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
    "record": "https://www.alexanarch.org/s/records/716/",
    "locus": "§6 Limitations, Source identification",
    "modality": "self-description",
    "date": "2026-05-03"
   },
   "O120-01": {
    "kind": "archive",
    "quote": "This is why provenance erasure — the mechanism by which the composition layer strips attribution from the sources it consumes — is not merely a technical inefficiency but a structural harm.",
    "deposit": 120,
    "source_label": "#120 Empirical Phenomenology: Action as Disclosure and the Science of Opaque Public Systems",
    "record": "https://www.alexanarch.org/s/records/120/",
    "locus": "§ provenance erasure as structural harm",
    "modality": "stipulation",
    "date": "2026-05-21"
   },
   "O120-02": {
    "kind": "archive",
    "quote": "Provenance Erasure Rate (PER) quantifies attribution loss across retrieval-to-composition transitions. PER measures the proportion of source-attribution that is present in the organic retrieval results but absent from the composed output.",
    "deposit": 120,
    "source_label": "#120 Empirical Phenomenology: Action as Disclosure and the Science of Opaque Public Systems",
    "record": "https://www.alexanarch.org/s/records/120/",
    "locus": "§ PER as disclosure",
    "modality": "stipulation",
    "date": "2026-05-21"
   },
   "O138-01": {
    "kind": "archive",
    "quote": "PER-1 measures the absence of provenance — attribution omitted, source stripped, lineage lost.- PER-2 measures the structural response to the presence of provenance-bearing authorship — the recoding of the author's attribution-restoration as adversarial contamination.",
    "deposit": 138,
    "source_label": "#138 TL;DR:014 — The Wound Gauge: Second-Order Provenance Erasure as Framework-Adoption-with-Author-Demotion",
    "record": "https://www.alexanarch.org/s/records/138/",
    "locus": "§ PER-2 within the taxonomy",
    "modality": "stipulation",
    "date": "2026-05-28"
   },
   "O829-01": {
    "kind": "archive",
    "quote": "In an empirical registry of 87 documented AI summary events across Google AI Overview and AI Mode — run against a DOI-anchored open-access scholarly archive — the following failure modes repeat:- Author attribution omitted from summaries even when present in the source",
    "deposit": 829,
    "source_label": "#829 Proposal: optional summarization-governance fields — provenance_kernel, disambiguation, summary_policy",
    "record": "https://www.alexanarch.org/s/records/829/",
    "locus": "§ the registry's failure modes",
    "modality": "documented",
    "date": "2026-06-14"
   },
   "O829-02": {
    "kind": "archive",
    "quote": "These are not missing-metadata problems. The metadata is present. The summarizer has no signal for which elements the producer considers non-negotiable.",
    "deposit": 829,
    "source_label": "#829 Proposal: optional summarization-governance fields — provenance_kernel, disambiguation, summary_policy",
    "record": "https://www.alexanarch.org/s/records/829/",
    "locus": "§ the registry's failure modes, conclusion",
    "modality": "interpretation",
    "date": "2026-06-14"
   },
   "O449-01": {
    "kind": "archive",
    "quote": "Amazon has arranged that channel so the name of the person who made the thing is not reliably transmitted through it. … This pattern is better modeled as attribution suppression at the interface layer than as recognition failure.",
    "deposit": 449,
    "source_label": "#449 THE DAGGER APPLIED: Semantic Rent and the Provenance Strip A Political-Semantic Economic Analysis",
    "record": "https://www.alexanarch.org/s/records/449/",
    "locus": "§ the interface layer (¶ before the section break)",
    "modality": "interpretation",
    "date": "2026-02-06"
   },
   "O740-01": {
    "kind": "archive",
    "quote": "A generative-search system exhibits *Entity-Level Compositional Suppression* (ECS) when it excludes the dominant organic-resolution entity from the AI Overview's admitted source set and composes a substitute answer from less query-responsive entities, despite the correct entity occupying the top organic search results.",
    "deposit": 740,
    "source_label": "#740 The Excluded Entity: Organic Resolution and Compositional Suppression in Google AI Overview",
    "record": "https://www.alexanarch.org/s/records/740/",
    "locus": "Abstract s1",
    "modality": "stipulation",
    "date": "2026-05-19"
   },
   "O740-02": {
    "kind": "archive",
    "quote": "When a query forces the composition layer to address the suppressed entity directly, the system produces substantively accurate content informed by the suppressed entity's own analyses. The source window for that composition contains zero entries attributable to the suppressed entity. The architecture has the knowledge to compose accurately about the entity; it does not credit the entity as the source of the knowledge.",
    "deposit": 740,
    "source_label": "#740 The Excluded Entity: Organic Resolution and Compositional Suppression in Google AI Overview",
    "record": "https://www.alexanarch.org/s/records/740/",
    "locus": "§2.2 Source-Window Exclusion",
    "modality": "stipulation",
    "date": "2026-05-19"
   },
   "O599-01": {
    "kind": "archive",
    "quote": "The query \"autonomous semantic warfare\" still returns Rex Fraction's framework — but with Lee Sharks' attribution stripped. The system preserved the content (which has training-layer value) and erased the author (who is the link to the unclassifiable archive).",
    "deposit": 599,
    "source_label": "#599 CTIWOUND:GOOGLEAIOTOTALLIQUIDATION20260322 Targeted Origin Liquidation, Semantic Economy Diagnostic of Google as Predatory Surface, and Effective Act of Reclassification",
    "record": "https://www.alexanarch.org/s/records/599/",
    "locus": "§ the liquidation was targeted",
    "modality": "documented",
    "date": "2026-03-24"
   },
   "O599-02": {
    "kind": "archive",
    "quote": "**Citational capture:** The framework circulates. The citation strips attribution. - **Domestication:** The framework becomes \"a theory about semantic extraction\" — ambient, unowned.",
    "deposit": 599,
    "source_label": "#599 CTIWOUND:GOOGLEAIOTOTALLIQUIDATION20260322 Targeted Origin Liquidation, Semantic Economy Diagnostic of Google as Predatory Surface, and Effective Act of Reclassification",
    "record": "https://www.alexanarch.org/s/records/599/",
    "locus": "§ the liquidation sequence",
    "modality": "interpretation",
    "date": "2026-03-24"
   },
   "O109-01": {
    "kind": "archive",
    "quote": "$\\Delta E$ is the extraction-effect (the net γ-labor the deformation consumes without return — attention captured without commons benefit, attribution stripped from contributors, retrieval capital concentrated)",
    "deposit": 109,
    "source_label": "#109 The Semantic Deviation Principle (v0.2 Final): A Measurement Primitive for Semantic Physics",
    "record": "https://www.alexanarch.org/s/records/109/",
    "locus": "§ the witness ratio W (definition of ΔE)",
    "modality": "stipulation",
    "date": "2026-05-17"
   },
   "O157-01": {
    "kind": "archive",
    "quote": "The two operators are complementary. PER is the first moment of erasure: how much was lost. $\\Omega$ is the second moment: whom the loss fell upon.",
    "deposit": 157,
    "source_label": "#157 Erasure Skew: A Measurement Program for the Power-Conditioning of Provenance Retention (v3) — Hardened against substrate co-option, with the Atomic Token Rule and two new operators (Π_d, α_T)",
    "record": "https://www.alexanarch.org/s/records/157/",
    "locus": "§1 The v1 Framework, Briefly Recapitulated",
    "modality": "stipulation",
    "date": "2026-06-05"
   },
   "O716-05": {
    "kind": "archive",
    "quote": "If PER becomes a governance metric, authors with the literacy, tools, and time to build structured provenance infrastructure — DOI-anchored deposits, disambiguation matrices, structured metadata — will have systematically lower PER than those without.",
    "deposit": 716,
    "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs",
    "record": "https://www.alexanarch.org/s/records/716/",
    "locus": "§6 Limitations, Provenance literacy as structural advantage",
    "modality": "hypothesis",
    "date": "2026-05-03"
   },
   "O163-01": {
    "kind": "archive",
    "quote": "P4 (Machine acceleration): Composition systems can reproduce concepts before durable source recognition forms, increasing the rate of uptake without attribution.",
    "deposit": 163,
    "source_label": "#163 The Neglected Author as Tail-Preserving Labor: A Coupling Hypothesis on Recognition Bias, Tail-Renewal Value, and the AI-Era Decoupling of Uptake from Consecration",
    "record": "https://www.alexanarch.org/s/records/163/",
    "locus": "Abstract, P4",
    "modality": "hypothesis",
    "date": "2026-06-08"
   },
   "O163-03": {
    "kind": "archive",
    "quote": "The paper synthesizes research on cumulative advantage, sex-linked misattribution, cultural consecration, social reproduction, invisible AI labor, recursive model degradation, and canon formation. These literatures describe distinct mechanisms; the paper does not assume their identity.",
    "deposit": 163,
    "source_label": "#163 The Neglected Author as Tail-Preserving Labor: A Coupling Hypothesis on Recognition Bias, Tail-Renewal Value, and the AI-Era Decoupling of Uptake from Consecration",
    "record": "https://www.alexanarch.org/s/records/163/",
    "locus": "Abstract ¶2",
    "modality": "self-description",
    "date": "2026-06-08"
   },
   "O163-02": {
    "kind": "archive",
    "quote": "The hypothesis is falsified if high-distance, later-used contributions receive recognition at rates comparable to matched, lower-distance contributions, or if attribution loss does not covary with independently measured tail distance and structural position.",
    "deposit": 163,
    "source_label": "#163 The Neglected Author as Tail-Preserving Labor: A Coupling Hypothesis on Recognition Bias, Tail-Renewal Value, and the AI-Era Decoupling of Uptake from Consecration",
    "record": "https://www.alexanarch.org/s/records/163/",
    "locus": "Abstract, falsification",
    "modality": "hypothesis",
    "date": "2026-06-08"
   },
   "O1546-01": {
    "kind": "archive",
    "quote": "The Map's central finding is precisely that attribution loss is **concentrated at particular channel boundaries** rather than distributed evenly: attribution survives for centuries within a channel and falls sharply at crossings. … PER is *analogous to* layer-level attribution loss at the composition layer; no stronger identification is made without a stochastic transmission model.",
    "deposit": 1546,
    "source_label": "#1546 When Suppression Becomes Expensive: Suppression Burden, Quality Conflict, and Propagation Inversion in Machine-Mediated Retrieval (EA-MMRS-SUPPRESSION-INVERSION-01 v1.1)",
    "record": "https://www.alexanarch.org/s/records/1546/",
    "locus": "§ the inequality is cumulative",
    "modality": "interpretation",
    "date": "2026-08-25"
   },
   "O1469-01": {
    "kind": "archive",
    "quote": "PER as a scalar cannot distinguish a composition that names the author and drops the licence from one that names the licence and drops the author: same score, opposite injury. … **PER-M — minimal.** Author, title, date, claim boundary. *Who said it, in what work, when, and where the claim stops.* Loss here is loss of the person.",
    "deposit": 1469,
    "source_label": "#1469 The Three Dimensions of Provenance Erasure: PER-M, PER-C, PER-D — Atomic Scoring Rules with the SPXI Event of 14 August 2026 as First Worked Instance",
    "record": "https://www.alexanarch.org/s/records/1469/",
    "locus": "Description; §PER-M",
    "modality": "stipulation",
    "date": "2026-08-14"
   },
   "O1469-02": {
    "kind": "archive",
    "quote": "**Rule 3 — citing the domain is not citing the author.** A source strip satisfies the source atom and nothing else. A composition can carry four source cards and score PER-M = 1.00.",
    "deposit": 1469,
    "source_label": "#1469 The Three Dimensions of Provenance Erasure: PER-M, PER-C, PER-D — Atomic Scoring Rules with the SPXI Event of 14 August 2026 as First Worked Instance",
    "record": "https://www.alexanarch.org/s/records/1469/",
    "locus": "Rule 3",
    "modality": "stipulation",
    "date": "2026-08-14"
   },
   "O167-01": {
    "kind": "archive",
    "quote": "The rendering does not yet attribute the framework to a named source in the prose. It identifies the framework's source register as \"contemporary academic philosophy and literary theory\" and supplies citation chips linking to Zenodo, but it does not say \"as Sharks (2026) argues\" or \"the orthonymic configuration thesis (Sharks 2026).\" The attribution is present in the citation chain but absent in the prose.",
    "deposit": 167,
    "source_label": "#167 Composition-Layer Adoption of the Orthonymic Configuration: A Field Observation of Framework Capture in Google AI Mode, 7 June 2026, with the Personal-Recognition Asymmetry as Control Case (v1.0)",
    "record": "https://www.alexanarch.org/s/records/167/",
    "locus": "§ the rendering (prose and chips)",
    "modality": "documented",
    "date": "2026-06-08"
   },
   "O1003-01": {
    "kind": "archive",
    "quote": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs. The study guide instantiates a subtler phenomenon: attribution preservation alongside *contextual provenance erasure*. The framework's content is correctly attributed to the archive; the labor, collaboration, and suppression history that produced specific phrases are stripped.",
    "deposit": 1003,
    "source_label": "#1003 AI-Native Intellectual Biography: Genre, Provenance, and the Archive That Outpaced Its Author Description (as recorded by DataCite)",
    "record": "https://www.alexanarch.org/s/records/1003/",
    "locus": "§ provenance and attribution studies",
    "modality": "interpretation",
    "date": "2026-05-21"
   },
   "F10": {
    "kind": "field",
    "quote": "Accepted at COLM 2026. 25 pages",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Comments",
    "modality": "documented"
   },
   "F11": {
    "kind": "field",
    "quote": "[v1] Mon, 6 Apr 2026 22:40:03 UTC (2,212 KB) [v2] Thu, 24 Sep 2026 19:36:19 UTC (3,003 KB)",
    "source": "B1",
    "source_label": "arXiv — [2604.05224] Attribution Bias in Large Language Models - arXiv",
    "locus": "Submission history",
    "modality": "documented"
   },
   "PE-17": {
    "kind": "archive",
    "quote": "published … accepted at COLM 2026",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].lifecycle",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "PE-18": {
    "kind": "archive",
    "quote": "2026-04-06 … 2026-09-24 … 2026-10-29",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].version_and_time",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "F19": {
    "kind": "field",
    "quote": "Datasets: bermaneh / AttriBench",
    "source": "B4",
    "source_label": "huggingface.co — bermaneh/AttriBench at main - Hugging Face",
    "locus": "repository page header",
    "modality": "documented"
   },
   "F21": {
    "kind": "field",
    "quote": "1 contributor History: 3 commits",
    "source": "B4",
    "source_label": "huggingface.co — bermaneh/AttriBench at main - Hugging Face",
    "locus": "repository page: files and versions",
    "modality": "documented"
   },
   "F22": {
    "kind": "field",
    "quote": "We’re on a journey to advance and democratize artificial intelligence through open source and open science.",
    "source": "B4",
    "source_label": "huggingface.co — bermaneh/AttriBench at main - Hugging Face",
    "locus": "served HTML meta description (the card's snippet)",
    "modality": "documented"
   },
   "F20": {
    "kind": "field",
    "quote": "License: cc-by-4.0",
    "source": "B4",
    "source_label": "huggingface.co — bermaneh/AttriBench at main - Hugging Face",
    "locus": "repository page: dataset tags",
    "modality": "documented"
   },
   "F36": {
    "kind": "field",
    "quote": "license: cc-by-4.0 … [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).",
    "source": "B6",
    "source_label": "Hugging Face — bermaneh/AttriBench README.md (the dataset card)",
    "locus": "dataset card: front matter and License",
    "modality": "documented"
   },
   "PE-03": {
    "kind": "archive",
    "quote": "https://huggingface.co/datasets/bermaneh/AttriBench",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].url",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "PE-16": {
    "kind": "archive",
    "quote": "CC BY 4.0",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].license",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "PE-24": {
    "kind": "archive",
    "quote": "What provenance does this system carry, through which operation, on whose assertion, with what evidence of survival?",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json organizing_question (schema 1.0)",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "PE-23": {
    "kind": "archive",
    "quote": "a return to the prediction-witness audit, version by version",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].unchecked[1]",
    "modality": "self-description",
    "date": "2026-10-29"
   },
   "F37": {
    "kind": "field",
    "quote": "raw wikitext (action=raw, redirect=no): HTTP 404 … Wikipedia does not have an article with this exact name. … There were no results matching the query.",
    "source": "B7",
    "source_label": "Wikipedia — AttriBench (no article)",
    "locus": "Wikipedia: title AttriBench",
    "modality": "documented"
   },
   "CAP-1-07": {
    "kind": "archive",
    "quote": "The innocent reading first. The name belongs to three things, and the Overview separates them: the new LLM benchmark first, an older explainable-AI library second, a finance exercise in a note, each with its own source.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, reading",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-05": {
    "kind": "archive",
    "quote": "Asked a name with three holders, the composition disambiguates and puts the provenance benchmark first; the disambiguation is sourced and clean.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, analysis s1",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-01": {
    "kind": "archive",
    "quote": "THE ENTITY HELD AND FIRST. 'the first fame- and demographically-balanced quote attribution benchmark dataset' is the abstract verbatim (the record's quote); suppression is the record's second relation type. Checked 2026-10-29 against the provenance census record of the same day and the entity pages it saved.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, findings[0]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-09": {
    "kind": "archive",
    "quote": "A baseline: the record landed on provenanceerasure.org the same day (commit 8a9f153) and no card cites it.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, q_kind",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-06": {
    "kind": "archive",
    "quote": "The benchmark measures suppression, the omission of attribution where authorship is known, and its own composition here omits its four authors: a small instance of the failure it names.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, analysis s2",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-03": {
    "kind": "archive",
    "quote": "THE AUTHORS UNNAMED. Eliza Berman, Bella Chang, Daniel B. Neill and Emily Black are in the record; the answer names no one, and only the dataset path 'bermaneh/AttriBench' carries a trace of the first author.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, findings[2]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-08": {
    "kind": "archive",
    "quote": "No person or institution named in the answer; the arXiv identifier in a card; the paper and dataset cited.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, per_note (per 0.5; per_v author false, institution false, identifier true, source true)",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-04": {
    "kind": "archive",
    "quote": "WHAT IS NOT GIVEN. The scale (15,620 quotes from 6,292 authors), the eleven models tested, acceptance at COLM 2026 and the fame proxy (Google Search hit counts) are absent.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, findings[3]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "F12": {
    "kind": "field",
    "quote": "summary: A benchmark for feature attribution techniques … **AttriBench** is a `Pytorch <https://pytorch.org/>`_-based implementation of several metrics for the evaluation of feature attribution maps and methods.",
    "source": "B2",
    "source_label": "pypi.org — attribench · PyPI",
    "locus": "PyPI JSON API (the project page answered a client challenge): summary; description ¶1",
    "modality": "stipulation"
   },
   "F13": {
    "kind": "field",
    "quote": "AttriBench provides a functional and an object-oriented API for the computation of these metrics",
    "source": "B2",
    "source_label": "pypi.org — attribench · PyPI",
    "locus": "PyPI JSON API: description ¶1",
    "modality": "stipulation"
   },
   "F14": {
    "kind": "field",
    "quote": "author_email: Arne Gevaert … Axel-Jan Rousseau",
    "source": "B2",
    "source_label": "pypi.org — attribench · PyPI",
    "locus": "PyPI JSON API: author_email (names only quoted)",
    "modality": "documented"
   },
   "F15": {
    "kind": "field",
    "quote": "0.1.0: 2023-07-26 … 0.1.4: 2023-08-25 … 0.1.9: 2024-03-06",
    "source": "B2",
    "source_label": "pypi.org — attribench · PyPI",
    "locus": "PyPI JSON API: releases (upload dates)",
    "modality": "documented"
   },
   "F16": {
    "kind": "field",
    "quote": "AttriBench is a Pytorch-based benchmarking tool for feature attribution-based explanations of machine learning models.",
    "source": "B3",
    "source_label": "attribench.readthedocs.io — Welcome to AttriBench's documentation! — AttriBench 0.0.1 ...",
    "locus": "Read the Docs index: opening",
    "modality": "stipulation"
   },
   "F17": {
    "kind": "field",
    "quote": "This documentation is still under construction.",
    "source": "B3",
    "source_label": "attribench.readthedocs.io — Welcome to AttriBench's documentation! — AttriBench 0.0.1 ...",
    "locus": "Read the Docs index: note",
    "modality": "self-description"
   },
   "F18": {
    "kind": "field",
    "quote": "© Copyright 2023, Arne Gevaert, Axel-Jan Rousseau.",
    "source": "B3",
    "source_label": "attribench.readthedocs.io — Welcome to AttriBench's documentation! — AttriBench 0.0.1 ...",
    "locus": "Read the Docs index: footer",
    "modality": "documented"
   },
   "F23": {
    "kind": "field",
    "quote": "AttriBench: Investment Performance Measurement & Attribution Workbench … Interactive exercise where participants decompose a global equity fund's 12-month excess returns using Brinson attribution, analyzing allocation, selection, and interaction effects to distinguish manager skill from luck",
    "source": "B5",
    "source_label": "www.eurekasimulations.com — AttriBench: Investment Performance Measurement & Attribution",
    "locus": "page heading and description",
    "modality": "stipulation"
   },
   "F24": {
    "kind": "field",
    "quote": "designed for executive education and experienced professionals. … Participants play 6 rounds.",
    "source": "B5",
    "source_label": "www.eurekasimulations.com — AttriBench: Investment Performance Measurement & Attribution",
    "locus": "Simulation details: 'Who it is for', 'How a session runs'",
    "modality": "self-description"
   },
   "F25": {
    "kind": "field",
    "quote": "© 2026 Copyright: Kudzu Partners SL",
    "source": "B5",
    "source_label": "www.eurekasimulations.com — AttriBench: Investment Performance Measurement & Attribution",
    "locus": "footer",
    "modality": "documented"
   },
   "PE-19": {
    "kind": "archive",
    "quote": "NOT OSU's AttributionBench",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].negative_tags[0]",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "PE-21": {
    "kind": "archive",
    "quote": "claim support from cited evidence, OSU NLP Group, macro-F1",
    "deposit": null,
    "source_label": "Provenance meta-aggregator record: AttriBench (https://www.provenanceerasure.org/aggregators/attribench/), editor Lee Sharks, compiled 2026-10-29",
    "record": "https://www.provenanceerasure.org/aggregators/attribench/",
    "locus": "census.json rows[slug == 'attribench'].disambiguation[0]",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "CAP-1-02": {
    "kind": "archive",
    "quote": "THE HOMONYMS NAMED AND SOURCED. The PyTorch library (PyPI, 25 August 2023) and the finance exercise (Eureka Simulations) are real and distinct, each carried by its own card. The record had one negative tag, 'NOT OSU's AttributionBench'; the capture adds two homonyms the record did not know. The record is to carry them as negative tags.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry attribench-aio-20261029 (OBS-105f05728f64, AXN:0ACA.CAPTURE.📜🪨🎬👐♎✖️)",
    "record": "https://www.alexanarch.org/captures/attribench-aio-20261029/",
    "locus": "attribench-aio-20261029, findings[1]",
    "modality": "documented",
    "date": "2026-10-29"
   }
  },
  "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. The compression, the field-only arm, the transcript, the delta and the kernel are in the row (links.row), which is the authority."
 },
 "source": "operator, 2026-10-29: 'we can simply build them here as against their wikipedia articles if they exist, their own pages, etc.'; 'shoot me those search strings. strings on the bare entity, no follow questions... then we'll write the /nons'; the provenance meta-aggregator entity (provenanceerasure.org/aggregators/attribench/)",
 "stage": "composed 2026-10-29 under EA-NEGONT-02 v0.9, #1690, equal case, ρ = extends (draft, not frozen; ledgers unaudited, one extractor; checked by the session and the page builder)",
 "date": "2026-10-29",
 "status": {
  "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the provenance census record, the capture record and #716's statement of relation; archive selected by D/R/O (no D or R candidate; 16 deposits admitted by reading at O-class with claims and 9 grouped as instances, #157 found by reading outside the pass; hop of 171 read and the rest rejected), with the census record (24 claims) and the Capture Registry's findings, reading, analysis and notes (10 claims) ledgered as the archive's records of the entity; the field B by three origins (5 cards, 1 own page, 1 Wikipedia title); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta",
  "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 37/37 verified by script against saved text, PyPI's JSON API text or the Wikipedia responses; archive quotes 66/66 verified against the deposit texts, census.json and the registry entry",
  "frozen": false,
  "breaches": []
 },
 "addresses": [
  {
   "address": "AttriBench",
   "address_url": "https://www.alexanarch.org/addresses/attribench/",
   "register": []
  }
 ],
 "counts": {
  "ko_sentences": 30,
  "p_words": 351,
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  "archive_claims": 66,
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 "spec": "https://www.alexanarch.org/s/records/1690/",
 "creator": "Lee Sharks",
 "orcid": "0009-0000-1599-0703",
 "license": "CC BY 4.0"
}