{
 "row": "academ-ai",
 "entity": "academ-ai",
 "address": "academ-ai",
 "addresses": [
  {
   "address": "academ-ai",
   "slug": "academ-ai-aio-20261029",
   "addr_id": "ADDR-b459d7aa5026",
   "obs_id": "OBS-e884288abc5d",
   "axn": "AXN:0AA4.CAPTURE.⏬🏺⛳🗼↙️🔺"
  }
 ],
 "epoch": "2026-10-29",
 "type": "C (public entity: a database of suspected undeclared AI use in published scholarship; its name shared by three education products, distinct entities)",
 "surface": "Google AI Overview (expanded)",
 "auth": "signed out, incognito",
 "status": {
  "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the archive's records of the entity (the MMRS meta-aggregator record of 2026-10-28, compiled 2026-10-29, and the Capture Registry's entry of 2026-10-29); no deposit names the entity (D/R: 0 candidates over 1,711 texts); O-class on 'documentation of undeclared AI use in academic publishing' (sourced: B1/B5, B7, B8) returns one candidate (#1540), not admitted; hop not run; one address, the field B its four cards with the entity's own pages (3) and the MMRS record's third-party source (1) admitted per the operator's ruling of 2026-10-29 and Wikipedia's absence recorded; no corrective exhibit; 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 cut from saved page text or a Capture Registry card snippet, 47/47 verified by script; archive quotes verified verbatim 40/40 against census.json, articles.py and the registry's entry as loaded); contested within the field and carried both ways: whether theses are in scope (F32 against F39, with MMRS-05, MMRS-18), the errata label (F36 against F41, with MMRS-06, MMRS-19, MMRS-24)",
  "frozen": false,
  "breaches": []
 },
 "spec": "EA-NEGONT-02 v0.9, #1690",
 "objects": {
  "T": {
   "path": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/T1-academ-ai-aio-20261029.txt",
   "sha256": "83dc8b424e0c29823a218c56900f5e88920faa3394e07c41ecc932413f50b53e",
   "words": 187,
   "cards": 4,
   "words_note": "one transcript at one address; words of the composition from the answer's first sentence to its closing offer, link targets and citation markers removed; the cards' titles and snippets excluded",
   "claims": [
    "T1 definition: 'Academ-AI most notably refers to a research study and framework documenting the undisclosed use of generative artificial intelligence in academic publishing.' (marker [1], the arXiv card)",
    "T2 the study: 'The paper titled \"Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing\" analyzes hundreds of examples of undeclared LLM (Large Language Model) phrasing across journals, conference proceedings, and textbooks.' (marker [1])",
    "T3 findings: 'It highlights how characteristic AI text patterns (such as leftover conversational prompts or disclaimers) slip past peer review, often appearing more frequently in outlets with high article processing charges (APCs) and citation metrics.' (marker [1])",
    "T4 the homonyms offered: 'If you are looking for an educational or study tool, the name may also refer to:' (under 'Similar-Sounding Platforms & Tools')",
    "T5 AcademAI: 'A guided AI training and certification console offering an AI fluency foundation course and mastery paths.' (marker [1], the academai.app card)",
    "T6 Academi.AI: 'An AI-powered study and chat tutor platform for personalized learning.' (marker [1], the Academi.AI card)",
    "T7 Academiq: 'An AI education ecosystem with tools for lesson planning, homework support, and exam preparation.' (marker [1], the Academiq card)",
    "T8 closer: 'Are you looking for the research paper on AI in publishing, or a specific student/teacher AI learning app? Let me know and I can share more details.'"
   ],
   "register": {
    "register": "data/EA-WG-CAPTURES-01.json",
    "slug": "academ-ai-aio-20261029",
    "obs_id": "OBS-e884288abc5d"
   },
   "transcripts": [
    {
     "slug": "academ-ai-aio-20261029",
     "address": "academ-ai",
     "addr_id": "ADDR-b459d7aa5026",
     "obs_id": "OBS-e884288abc5d",
     "axn": "AXN:0AA4.CAPTURE.⏬🏺⛳🗼↙️🔺",
     "sha256": "83dc8b424e0c29823a218c56900f5e88920faa3394e07c41ecc932413f50b53e",
     "sha256_of": "the registry's transcript string (data/EA-WG-CAPTURES-01.json, entries[slug].transcript), UTF-8",
     "words": 187,
     "words_note": "as above",
     "cards": 4,
     "card_rail": [
      "arXiv — [2411.15218] Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing",
      "Academi.AI — Academi.AI",
      "academai.app — AcademAI — Premium AI Training Console",
      "Academiq — AI Education Platform | AI Tools for Teachers & Students – Academiq"
     ],
     "claims": [
      "T1 definition: 'Academ-AI most notably refers to a research study and framework documenting the undisclosed use of generative artificial intelligence in academic publishing.' (marker [1], the arXiv card)",
      "T2 the study: 'The paper titled \"Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing\" analyzes hundreds of examples of undeclared LLM (Large Language Model) phrasing across journals, conference proceedings, and textbooks.' (marker [1])",
      "T3 findings: 'It highlights how characteristic AI text patterns (such as leftover conversational prompts or disclaimers) slip past peer review, often appearing more frequently in outlets with high article processing charges (APCs) and citation metrics.' (marker [1])",
      "T4 the homonyms offered: 'If you are looking for an educational or study tool, the name may also refer to:' (under 'Similar-Sounding Platforms & Tools')",
      "T5 AcademAI: 'A guided AI training and certification console offering an AI fluency foundation course and mastery paths.' (marker [1], the academai.app card)",
      "T6 Academi.AI: 'An AI-powered study and chat tutor platform for personalized learning.' (marker [1], the Academi.AI card)",
      "T7 Academiq: 'An AI education ecosystem with tools for lesson planning, homework support, and exam preparation.' (marker [1], the Academiq card)",
      "T8 closer: 'Are you looking for the research paper on AI in publishing, or a specific student/teacher AI learning app? Let me know and I can share more details.'"
     ],
     "markers": "Inline links are google.com/goto tokens, opaque; every inline marker reads [1] and links a different card; the paste is the Overview expanded (operator's default of 2026-10-01 and the registry's rule of 2026-10-18 on the 'AI Mode Conversation' header); signed out, incognito (operator's default of 2026-10-14). One turn. Four cards.",
     "register": {
      "registry": "data/EA-WG-CAPTURES-01.json",
      "slug": "academ-ai-aio-20261029",
      "obs_id": "OBS-e884288abc5d",
      "date": "2026-10-29",
      "surface": "Google AI Overview",
      "auth": "signed out, incognito"
     },
     "path": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/T1-academ-ai-aio-20261029.txt"
    }
   ]
  },
  "KO": {
   "title": "Academ-AI",
   "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 (brief rebuild/negative-of-the-negative/v09/BRIEF-NEW-ROW-AGGREGATOR.md); not frozen; ledgers unaudited",
   "sentences": [
    {
     "n": 1,
     "para": 0,
     "sense": "what it is",
     "text": "Academ-AI is Alex Glynn's database of published scholarship with suspected undeclared use of AI, identified by chatbot phrasing and quoted with the phrases highlighted (MMRS record), and its own About page says it \"documents the adverse effects of artificial intelligence (AI) in academia, particularly suspected instances of AI being used to author research without appropriate declaration\" (academ-ai.info/about).",
     "claims": [
      "MMRS-01",
      "F29"
     ]
    },
    {
     "n": 2,
     "para": 0,
     "sense": "what it is",
     "text": "Its home page heads it \"Suspected undeclared AI usage in the academic literature\" (academ-ai.info), and in his dataset paper Glynn writes \"I maintain a repository of examples known as Academ-AI\", which \"documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots\" (Glynn, arXiv:2411.15218).",
     "claims": [
      "F28",
      "F11",
      "F1"
     ]
    },
    {
     "n": 3,
     "para": 0,
     "sense": "what it is",
     "text": "The MMRS record keeps the database apart from the paper, \"NOT only the dataset paper (arXiv:2411.15218): the paper analyses the database's first 768 cases\", and records its kind as database and its role as monitoring (MMRS record); the Capture Registry gives it as \"a database of cases, with an errata page and a publishers page\" (Capture Registry, 2026-10-29).",
     "claims": [
      "MMRS-29",
      "MMRS-14",
      "MMRS-15",
      "CAP-1-01"
     ]
    },
    {
     "n": 4,
     "para": 1,
     "sense": "who makes it",
     "text": "Alex Glynn made it: eDiscovery Today names him \"Research Literacy and Communications Instructor at the Kornhauser Health Sciences Library, University of Louisville\" (eDiscovery Today, 27 August 2025), the MMRS record gives him so, as a research literacy instructor, on that third-party basis (MMRS record), and the paper, \"Authors: Alex Glynn\", adds a second affiliation, the Meta-Research Innovation Center at Stanford (METRICS) (arXiv:2411.15218).",
     "claims": [
      "F38",
      "MMRS-02",
      "MMRS-13",
      "F10",
      "F12"
     ]
    },
    {
     "n": 5,
     "para": 2,
     "sense": "how cases enter",
     "text": "An entry is a published work whose text carries phrasing that strongly suggests a chatbot wrote it, without any disclosure (MMRS record); the site's articles \"have been identified based on phrases that strongly suggest AI use (highlighted in each quoted passage)\" (academ-ai.info/about), and the record holds that it preserves the original outputs, \"quoted passages with the phrases highlighted\" (MMRS record).",
     "claims": [
      "MMRS-03",
      "MMRS-16",
      "F30",
      "MMRS-04",
      "MMRS-23"
     ]
    },
    {
     "n": 6,
     "para": 2,
     "sense": "how cases enter",
     "text": "In the paper's method, \"Phrases characteristic of AI-generated text were used to query Google Scholar\" and \"Each result was manually examined and included or excluded according to the following criteria\" (arXiv:2411.15218); readers may also send a suspected case with its citation and passage (academ-ai.info/about).",
     "claims": [
      "F13",
      "F14",
      "F31"
     ]
    },
    {
     "n": 7,
     "para": 2,
     "sense": "scope",
     "text": "The scope is stated two ways at two dates: the About page now documents \"journal articles, conference papers/proceedings, books/chapters, and dissertations/theses only\", with preprints, blog posts and other media out of scope (academ-ai.info/about, read 2026-10-29), while eDiscovery Today reported theses out of scope in August 2025 (eDiscovery Today) and the MMRS record follows that report, \"theses, preprints and blog posts are excluded\" (MMRS record); the paper excluded \"Any medium other than journal articles, conference papers, and books\" (arXiv:2411.15218).",
     "claims": [
      "F32",
      "F39",
      "MMRS-05",
      "MMRS-18",
      "F15"
     ]
    },
    {
     "n": 8,
     "para": 2,
     "sense": "metadata as published",
     "text": "It cites each publication with the publisher's metadata, even where implausible, and leaves the errors standing: \"Since the purpose of Academ-AI is to highlight publishing oversights, no attempt will be made to resolve these errors\" (academ-ai.info/about).",
     "claims": [
      "F35"
     ]
    },
    {
     "n": 9,
     "para": 3,
     "sense": "its size",
     "text": "Its size is dated: 772 examples in August 2025, 633 journal articles, 111 conference papers and 28 books and chapters (eDiscovery Today; MMRS record), and the record leaves \"the current count (the site refuses our fetcher)\" unchecked (MMRS record).",
     "claims": [
      "F40",
      "MMRS-17",
      "MMRS-22"
     ]
    },
    {
     "n": 10,
     "para": 3,
     "sense": "the dataset paper",
     "text": "The dataset paper was posted on 20 November 2024 as \"Suspected Undeclared Use of Artificial Intelligence in the Academic Literature: An Analysis of the Academ-AI Dataset\" and revised on 15 November 2025, with a journal reference in Springer's Lecture Notes in Networks and Systems (IntelliSys 2026); its dataset is \"768 published documents: 633 journal articles, 107 conference papers, and 28 book chapters\" (arXiv:2411.15218), which the MMRS record reads as the database's first 768 cases (MMRS record).",
     "claims": [
      "F8",
      "F27",
      "F9",
      "F16",
      "MMRS-08"
     ]
    },
    {
     "n": 11,
     "para": 3,
     "sense": "the dataset paper",
     "text": "The paper finds the problem \"widespread, penetrating the journals, conference proceedings, and textbooks of highly respected publishers\", and \"44% of the documents in the Academ-AI dataset make a similar reference to their training cutoff\" (arXiv:2411.15218).",
     "claims": [
      "F3",
      "F17"
     ]
    },
    {
     "n": 12,
     "para": 3,
     "sense": "where it appears",
     "text": "Undeclared AI \"seems to appear in journals with higher citation metrics and higher article processing charges (APCs)\"; \"approximately one in four of these evaded detection by a major publisher with an explicit policy against such content\", and the journals with the oversights \"are paradoxically paid more for editorial processing and achieve greater academic impact\" (arXiv:2411.15218), which the MMRS record carries as undeclared use found more often in journals with higher citation metrics and higher article charges (MMRS record).",
     "claims": [
      "F4",
      "F20",
      "F21",
      "MMRS-08"
     ]
    },
    {
     "n": 13,
     "para": 4,
     "sense": "after publication",
     "text": "After publication \"An extremely small minority of cases are corrected\", the corrections often insufficient: 33 documents (4.3%) were altered, and \"Formal alteration is best practice, while stealth alteration compromises transparency and scientific integrity\" (arXiv:2411.15218); the site marks silent publisher corrections \"stealth revision\" (academ-ai.info/about), and its errata page, as eDiscovery Today reported it and the MMRS record gives it, separates corrections with formal explanations from \"stealth corrections\" (eDiscovery Today; MMRS record).",
     "claims": [
      "F5",
      "F18",
      "F19",
      "F36",
      "F41",
      "MMRS-06",
      "MMRS-19"
     ]
    },
    {
     "n": 14,
     "para": 4,
     "sense": "by publisher",
     "text": "A publishers page counts cases by publisher: in August 2025 IEEE led with 90 and Elsevier followed with 34 (eDiscovery Today; MMRS record).",
     "claims": [
      "F42",
      "MMRS-07"
     ]
    },
    {
     "n": 15,
     "para": 5,
     "sense": "the norm",
     "text": "The norm it holds publications to is the publishing community's: \"such usage must be declared in the published article\" (arXiv:2411.15218); use \"should not be disqualifying in and of itself, but failure to declare it should be considered a serious problem\" (academ-ai.info/about), and \"Publishers must enforce their policies against undeclared AI usage in cases that are detectable\" (arXiv:2411.15218).",
     "claims": [
      "F2",
      "F34",
      "F7"
     ]
    },
    {
     "n": 16,
     "para": 6,
     "sense": "the selection's limit",
     "text": "Its unit is \"a detectable trace\" (MMRS record): the examples were identifiable by turns of phrase \"only reproduced in a manuscript when authors copy text directly from an AI system with little to no scrutiny\", and the 768 \"likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable\" (arXiv:2411.15218), which the record gives as much undeclared use leaving no phrase behind (MMRS record).",
     "claims": [
      "MMRS-09",
      "F22",
      "F6",
      "MMRS-10"
     ]
    },
    {
     "n": 17,
     "para": 6,
     "sense": "the selection's limit",
     "text": "The site says \"it is impossible to say just how extensive the failure to report AI usage is\" (academ-ai.info/about); the paper holds, as a hypothesis, that lightly edited AI text may be abundant and undetectable, and that this \"should not deter us from studying and contending with the examples that are observable\", and it states its coverage: manual search, data that \"may not be fully up to date or representative\", and \"English-language literature indexed in online databases accessible to the author, principally Google Scholar\" (arXiv:2411.15218).",
     "claims": [
      "F33",
      "F23",
      "F24",
      "F25"
     ]
    },
    {
     "n": 18,
     "para": 6,
     "sense": "the data dump",
     "text": "Its data are deposited as \"Academ-AI data dump\" on Figshare (10.6084/m9.figshare.29650481.v2, 2025) (arXiv:2411.15218).",
     "claims": [
      "F26"
     ]
    },
    {
     "n": 19,
     "para": 7,
     "sense": "place in the field",
     "text": "eDiscovery Today sets it beside the site that tracks AI hallucinations in case filings, \"There’s also one tracking AI use in academic papers\" (eDiscovery Today), and the MMRS record places it with the Problematic Paper Screener as a record of machine composition entering the scholarly record unattributed, where \"the machine is the uncredited author, and the publisher is where the reception happens\", the neighbour that \"reads by phrase where the screener reads by detector\" (MMRS record).",
     "claims": [
      "F37",
      "MMRS-11",
      "MMRS-12"
     ]
    },
    {
     "n": 20,
     "para": 8,
     "sense": "the lifecycle and the observation",
     "text": "Its lifecycle is active, held apart from the observation that saw it, by the record's rule that \"lifecycle is kept apart from the observation that saw it\": on 2026-10-28 the record's fetch was \"blocked (robots.txt disallows our fetcher)\" (MMRS record).",
     "claims": [
      "MMRS-20",
      "MMRS-31",
      "MMRS-21"
     ]
    },
    {
     "n": 21,
     "para": 9,
     "sense": "the name and its homonyms",
     "text": "Three education products share the sound of the name and are distinct entities, \"NOT AcademAI, Academi.AI or Academiq (education products)\" (MMRS record): AcademAI (academai.app), \"Independent premium training for mastering Microsoft Copilot, ChatGPT, OpenAI, Perplexity, Claude, Grok, Ollama Local AI Lab, Codex, APIs, and practical AI workflows\", with \"the free AI Fluency foundation course\", an \"Independent educational project\" (academai.app); Academi.AI, an \"AI-powered study assistant\" with a chat tutor (its card); Academiq, a \"unified AI education ecosystem with 150+ tools for teachers, students, parents, and institutions\" (its card); the record glosses them as a training console, a study tutor and an education platform (MMRS record).",
     "claims": [
      "MMRS-28",
      "F44",
      "F45",
      "F46",
      "F43",
      "F47",
      "MMRS-25",
      "MMRS-26",
      "MMRS-27"
     ]
    },
    {
     "n": 22,
     "para": 9,
     "sense": "the name and its homonyms",
     "text": "The Capture Registry observed the three as \"the name's collision neighbours as the composition layer holds them\", which the record had not had, and the record took them into its disambiguation, where the entity itself is \"Alex Glynn, undeclared AI use, journal articles, errata, stealth corrections\" (Capture Registry, 2026-10-29; MMRS record).",
     "claims": [
      "CAP-1-05",
      "CAP-1-06",
      "CAP-1-09",
      "MMRS-24"
     ]
    },
    {
     "n": 23,
     "para": 10,
     "sense": "its reception",
     "text": "At the address 'academ-ai' Google's AI Overview gave the paper as the entity, \"a research study and framework\", the site not among its cards, and \"Alex Glynn is not named\"; the registry reads the composition as made \"from the paper written about it, without its maker\", among three commercial homonyms, at a name its own site does not serve to crawlers, a finding checked against the MMRS record and the entity's own pages (Capture Registry, 2026-10-29; MMRS record).",
     "claims": [
      "CAP-1-02",
      "CAP-1-04",
      "CAP-1-08",
      "CAP-1-03",
      "CAP-1-07",
      "MMRS-30"
     ]
    }
   ],
   "joins": [
    {
     "act": "order of senses",
     "sentences": "1–23",
     "claims": [],
     "note": "equal case (§4.7): the entity's dependency order with both ledgers together, by genealogy within each sense: what it is (¶0), who makes it (¶1), how cases enter, scope and metadata (¶2), its size and the dataset paper with its findings (¶3), after publication and by publisher (¶4), the norm (¶5), the selection's limit and the data (¶6), place in the field (¶7), the lifecycle and the observation (¶8), the name and its homonyms (¶9), its reception (¶10)"
    },
    {
     "act": "holding together (contested)",
     "sentences": [
      7
     ],
     "claims": [
      "F32",
      "F39",
      "MMRS-05",
      "MMRS-18",
      "F15"
     ],
     "note": "the scope at two dates composed with their sources, neither lowered: the About page as read 2026-10-29 and eDiscovery Today's report of August 2025, which the record follows; the paper's criteria beside them"
    },
    {
     "act": "holding together (contested)",
     "sentences": [
      13
     ],
     "claims": [
      "F36",
      "F41",
      "MMRS-06",
      "MMRS-19"
     ],
     "note": "the site's 'stealth revision' beside 'stealth corrections' as reported in 2025 and carried by the record"
    },
    {
     "act": "coordinating",
     "sentences": [
      9,
      10
     ],
     "claims": [
      "F40",
      "MMRS-17",
      "F16",
      "MMRS-08"
     ],
     "note": "the two counts kept at their dates: 772 (August 2025, third-party) and the paper's 768 (633, 107, 28); not reconciled"
    },
    {
     "act": "keeping apart",
     "sentences": [
      21,
      22
     ],
     "claims": [
      "MMRS-28",
      "F43",
      "F44",
      "F47"
     ],
     "note": "the homonyms composed as distinct entities with their own sources, not as senses of the name"
    }
   ],
   "plan": {
    "order": [
     "MMRS-01",
     "F29",
     "F28",
     "F11",
     "F1",
     "MMRS-29",
     "MMRS-14",
     "MMRS-15",
     "CAP-1-01",
     "F38",
     "MMRS-02",
     "MMRS-13",
     "F10",
     "F12",
     "MMRS-03",
     "MMRS-16",
     "F30",
     "MMRS-04",
     "MMRS-23",
     "F13",
     "F14",
     "F31",
     "F32",
     "F39",
     "MMRS-05",
     "MMRS-18",
     "F15",
     "F35",
     "F40",
     "MMRS-17",
     "MMRS-22",
     "F8",
     "F27",
     "F9",
     "F16",
     "MMRS-08",
     "F3",
     "F17",
     "F4",
     "F20",
     "F21",
     "F5",
     "F18",
     "F19",
     "F36",
     "F41",
     "MMRS-06",
     "MMRS-19",
     "F42",
     "MMRS-07",
     "F2",
     "F34",
     "F7",
     "MMRS-09",
     "F22",
     "F6",
     "MMRS-10",
     "F33",
     "F23",
     "F24",
     "F25",
     "F26",
     "F37",
     "MMRS-11",
     "MMRS-12",
     "MMRS-20",
     "MMRS-31",
     "MMRS-21",
     "MMRS-28",
     "F44",
     "F45",
     "F46",
     "F43",
     "F47",
     "MMRS-25",
     "MMRS-26",
     "MMRS-27",
     "CAP-1-05",
     "CAP-1-06",
     "CAP-1-09",
     "MMRS-24",
     "CAP-1-02",
     "CAP-1-04",
     "CAP-1-08",
     "CAP-1-03",
     "CAP-1-07",
     "MMRS-30"
    ],
    "marks": {},
    "conflicts": [],
    "cuts": []
   },
   "field_claims": {
    "F1": {
     "source": "B1",
     "locus": "Abstract",
     "quote": "Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots.",
     "claim": "what it is: a documentation of suspected undeclared AI use in the literature, discernible mainly by chatbot verbiage",
     "modality": "stipulation",
     "kind": "definition",
     "kernel": true,
     "const": false,
     "lineage": "what it is",
     "quote_ok": true,
     "checked_against": "saved text B1.txt (sha256 5504faf64b4ef0c8…), whitespace-normalised"
    },
    "F2": {
     "source": "B1",
     "locus": "Abstract",
     "quote": "The consensus of the academic publishing community is that such usage must be declared in the published article.",
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    },
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     "source": "B1",
     "locus": "Abstract",
     "quote": "This analysis of the first 768 examples collected reveals that the problem is widespread, penetrating the journals, conference proceedings, and textbooks of highly respected publishers.",
     "claim": "the paper analyses the first 768 examples: the problem is widespread, in journals, proceedings and textbooks of highly respected publishers",
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     "source": "B1",
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     "quote": "Undeclared AI seems to appear in journals with higher citation metrics and higher article processing charges (APCs), precisely those outlets that should theoretically have the resources and expertise to avoid such oversights.",
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     "quote": "An extremely small minority of cases are corrected post publication, and the corrections are often insufficient to rectify the problem.",
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     "modality": "documented",
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     "locus": "Abstract",
     "quote": "The 768 examples analyzed here likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable.",
     "claim": "the governing limit: the 768 are likely a small fraction; much undeclared AI may be undetectable",
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     "source": "B1",
     "locus": "Abstract",
     "quote": "Publishers must enforce their policies against undeclared AI usage in cases that are detectable; this is the best defense currently available to the academic publishing community against the proliferation of undisclosed AI.",
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     "quote": "[Submitted on 20 Nov 2024 ( v1 ), last revised 15 Nov 2025 (this version, v2)]",
     "claim": "the paper: v1 20 November 2024, v2 15 November 2025",
     "modality": "documented",
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    },
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     "source": "B1",
     "locus": "Journal reference",
     "quote": "In: Arai, K., Lorenz, P. (eds) Intelligent Systems and Applications. IntelliSys 2026. Lecture Notes in Networks and Systems, vol 2077. Springer, Cham",
     "claim": "the paper's journal reference: IntelliSys 2026, Springer LNNS vol. 2077",
     "modality": "documented",
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     "source": "B1",
     "locus": "Authors",
     "quote": "Authors: Alex Glynn",
     "claim": "the paper's author: Alex Glynn",
     "modality": "documented",
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    },
    "F11": {
     "source": "B5",
     "locus": "§1 Background",
     "quote": "I maintain a repository of examples known as Academ-AI (https://www.academ-ai.info/).",
     "claim": "Glynn maintains a repository of examples known as Academ-AI at academ-ai.info",
     "modality": "self-description",
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    },
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     "source": "B5",
     "locus": "title page (affiliations)",
     "quote": "Kornhauser Health Sciences Library, University of Louisville, Louisville, KY, United States of America 2 Meta-Research Innovation Center at Stanford (METRICS), Stanford University",
     "claim": "the author's affiliations: Kornhauser Health Sciences Library, Louisville; METRICS, Stanford",
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    },
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     "source": "B5",
     "locus": "§2.1 Data collection",
     "quote": "Phrases characteristic of AI-generated text were used to query Google Scholar.",
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     "modality": "documented",
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    },
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     "source": "B5",
     "locus": "§2.1 Data collection",
     "quote": "Each result was manually examined and included or excluded according to the following criteria.",
     "claim": "each result is examined by hand against inclusion and exclusion criteria",
     "modality": "documented",
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    },
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     "source": "B5",
     "locus": "§2.1.2 Exclusion criteria",
     "quote": "Any medium other than journal articles, conference papers, and books, such as preprints or technical reports.",
     "claim": "the paper excludes any medium other than journal articles, conference papers and books",
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     "const": false,
     "lineage": "scope: what is admitted",
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     "checked_against": "saved text B5.txt (sha256 ba37182f92639620…), whitespace-normalised"
    },
    "F16": {
     "source": "B5",
     "locus": "§3 Results",
     "quote": "The dataset used for the present investigation comprised excerpts from 768 published documents: 633 journal articles, 107 conference papers, and 28 book chapters.",
     "claim": "the paper's dataset: 768 documents, 633 journal articles, 107 conference papers, 28 book chapters",
     "modality": "documented",
     "kind": "fact",
     "kernel": true,
     "const": false,
     "lineage": "its size",
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    },
    "F17": {
     "source": "B5",
     "locus": "§3.1.1 Since my last update",
     "quote": "44% of the documents in the Academ-AI dataset make a similar reference to their training cutoff.",
     "claim": "44% of the documents refer to a training cutoff",
     "modality": "documented",
     "kind": "fact",
     "kernel": false,
     "const": false,
     "lineage": "the tells",
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    },
    "F18": {
     "source": "B5",
     "locus": "§3.3 Corrections and retractions",
     "quote": "Thirty-three documents with suspected AI-generated content (4.3%) have been altered post publication",
     "claim": "33 documents (4.3%) altered after publication",
     "modality": "documented",
     "kind": "fact",
     "kernel": false,
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    },
    "F19": {
     "source": "B5",
     "locus": "§3.3 Corrections and retractions",
     "quote": "Formal alteration is best practice, while stealth alteration compromises transparency and scientific integrity.",
     "claim": "formal alteration best practice; stealth alteration compromises transparency and integrity",
     "modality": "interpretation",
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    },
    "F20": {
     "source": "B5",
     "locus": "§3.8 Summary of findings",
     "quote": "approximately one in four of these evaded detection by a major publisher with an explicit policy against such content.",
     "claim": "about one in four evaded a major publisher with an explicit policy",
     "modality": "documented",
     "kind": "fact",
     "kernel": false,
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     "lineage": "where it appears: higher APCs and citation metrics",
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    },
    "F21": {
     "source": "B5",
     "locus": "§3.8 Summary of findings",
     "quote": "the journals committing editorial oversights concerning AI are paradoxically paid more for editorial processing and achieve greater academic impact.",
     "claim": "the journals with the oversights are paid more for editorial processing and have greater impact",
     "modality": "interpretation",
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     "lineage": "where it appears: higher APCs and citation metrics",
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    },
    "F22": {
     "source": "B5",
     "locus": "§4.1 Dark AI",
     "quote": "The examples on Academ-AI were predominantly identifiable due to characteristic turns of phrase, which are only reproduced in a manuscript when authors copy text directly from an AI system with little to no scrutiny.",
     "claim": "the examples were identifiable by turns of phrase that survive only where text is copied with little scrutiny",
     "modality": "self-description",
     "kind": "fact",
     "kernel": false,
     "const": false,
     "lineage": "the selection's limit: the detectable trace",
     "quote_ok": true,
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    },
    "F23": {
     "source": "B5",
     "locus": "§4.1 Dark AI",
     "quote": "While this hypothesis is deeply troubling, it should not deter us from studying and contending with the examples that are observable",
     "claim": "the hypothesis of abundant undetectable edited AI text should not deter study of the observable examples",
     "modality": "hypothesis",
     "kind": "interpretation",
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     "const": false,
     "lineage": "the selection's limit: the detectable trace",
     "quote_ok": true,
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    },
    "F24": {
     "source": "B5",
     "locus": "§4.2 Limitations",
     "quote": "Since Academ-AI relies on manual search and examination, data may not be fully up to date or representative of undeclared AI in the literature as a whole.",
     "claim": "stated limit: manual search; data may not be up to date or representative",
     "modality": "self-description",
     "kind": "fact",
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     "const": false,
     "lineage": "the selection's limit: coverage",
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    },
    "F25": {
     "source": "B5",
     "locus": "§4.2 Limitations",
     "quote": "Coverage is limited to English-language literature indexed in online databases accessible to the author, principally Google Scholar.",
     "claim": "stated limit: English-language literature in databases accessible to the author, principally Google Scholar",
     "modality": "self-description",
     "kind": "fact",
     "kernel": false,
     "const": false,
     "lineage": "the selection's limit: coverage",
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    },
    "F26": {
     "source": "B5",
     "locus": "Declarations, Data availability",
     "quote": "Glynn A. Academ-AI data dump. 10.6084/m9.figshare.29650481.v2 Figshare, 2025.",
     "claim": "a data dump on Figshare (10.6084/m9.figshare.29650481.v2), 2025",
     "modality": "documented",
     "kind": "fact",
     "kernel": false,
     "const": false,
     "lineage": "the data dump",
     "quote_ok": true,
     "checked_against": "saved text B5.txt (sha256 ba37182f92639620…), whitespace-normalised"
    },
    "F27": {
     "source": "B5",
     "locus": "References, 1",
     "quote": "Glynn A. Suspected Undeclared Use of Artificial Intelligence in the Academic Literature: An Analysis of the Academ-AI Dataset. arXiv. Preprint posted online November 20, 2024.",
     "claim": "v1's title: Suspected Undeclared Use of AI in the Academic Literature: An Analysis of the Academ-AI Dataset (20 November 2024)",
     "modality": "documented",
     "kind": "genealogy",
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    },
    "F28": {
     "source": "B6",
     "locus": "home page",
     "quote": "Welcome to Academ-AI Suspected undeclared AI usage in the academic literature",
     "claim": "the site's own heading: suspected undeclared AI usage in the academic literature",
     "modality": "stipulation",
     "kind": "definition",
     "kernel": true,
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     "lineage": "what it is",
     "quote_ok": true,
     "checked_against": "saved text B6.txt (sha256 2830daf183daabbb…), whitespace-normalised"
    },
    "F29": {
     "source": "B7",
     "locus": "About ¶1",
     "quote": "Academ-AI documents the adverse effects of artificial intelligence (AI) in academia, particularly suspected instances of AI being used to author research without appropriate declaration.",
     "claim": "what it is: it documents AI's adverse effects in academia, particularly suspected undeclared AI authorship of research",
     "modality": "stipulation",
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    },
    "F30": {
     "source": "B7",
     "locus": "About ¶2",
     "quote": "The articles listed on this site have been identified based on phrases that strongly suggest AI use (highlighted in each quoted passage).",
     "claim": "entries are identified by phrases strongly suggesting AI use, highlighted in each quoted passage",
     "modality": "stipulation",
     "kind": "function",
     "kernel": true,
     "const": false,
     "lineage": "how cases enter",
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    },
    "F31": {
     "source": "B7",
     "locus": "About ¶3",
     "quote": "If you suspect the use of AI in a published research article, please reach out with:",
     "claim": "readers may submit suspected cases (citation, passage, name if credited)",
     "modality": "documented",
     "kind": "function",
     "kernel": false,
     "const": false,
     "lineage": "how cases enter",
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    },
    "F32": {
     "source": "B7",
     "locus": "About ¶4",
     "quote": "At present, I am documenting journal articles, conference papers/proceedings, books/chapters, and dissertations/theses only . Preprints, blog posts, and other media are out of scope at least for the time being.",
     "claim": "scope now: journal articles, conference papers, books and chapters, dissertations and theses; preprints, blog posts and other media out of scope",
     "modality": "stipulation",
     "kind": "definition",
     "kernel": true,
     "const": false,
     "lineage": "scope: what is admitted",
     "quote_ok": true,
     "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
    },
    "F33": {
     "source": "B7",
     "locus": "Purpose",
     "quote": "it is impossible to say just how extensive the failure to report AI usage is, considering that many of its outputs may be undetectable, particularly if edited by human authors.",
     "claim": "the extent cannot be said; many outputs may be undetectable, especially if edited",
     "modality": "self-description",
     "kind": "fact",
     "kernel": false,
     "const": false,
     "lineage": "the selection's limit: the detectable trace",
     "quote_ok": true,
     "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
    },
    "F34": {
     "source": "B7",
     "locus": "Purpose",
     "quote": "the use of generative AI should not be disqualifying in and of itself, but failure to declare it should be considered a serious problem.",
     "claim": "use is not disqualifying; failure to declare is a serious problem",
     "modality": "interpretation",
     "kind": "interpretation",
     "kernel": false,
     "const": false,
     "lineage": "the norm: declaration",
     "quote_ok": true,
     "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
    },
    "F35": {
     "source": "B7",
     "locus": "Metadata",
     "quote": "Since the purpose of Academ-AI is to highlight publishing oversights, no attempt will be made to resolve these errors.",
     "claim": "publications cited with the publisher's metadata, even implausible; errors not resolved",
     "modality": "stipulation",
     "kind": "function",
     "kernel": false,
     "const": false,
     "lineage": "metadata as published",
     "quote_ok": true,
     "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
    },
    "F36": {
     "source": "B7",
     "locus": "Post-publication changes",
     "quote": "Occasionally, publishers appear to have retroactively corrected LLM-induced errors without informing readers. These instances are marked as “stealth revision.”",
     "claim": "silent publisher corrections are marked 'stealth revision'",
     "modality": "stipulation",
     "kind": "definition",
     "kernel": false,
     "const": false,
     "lineage": "after publication: corrections, formal and stealth",
     "quote_ok": true,
     "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
    },
    "F37": {
     "source": "B8",
     "locus": "¶1",
     "quote": "There’s also one tracking AI use in academic papers.",
     "claim": "a site tracking AI use in academic papers, beside the one tracking AI hallucinations in case filings",
     "modality": "interpretation",
     "kind": "attribution",
     "kernel": false,
     "const": false,
     "lineage": "its neighbours",
     "quote_ok": true,
     "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
    },
    "F38": {
     "source": "B8",
     "locus": "¶4",
     "quote": "The creator of this site is Alex Glynn, MA, Research Literacy and Communications Instructor at the Kornhauser Health Sciences Library, University of Louisville.",
     "claim": "the creator: Alex Glynn, research literacy and communications instructor, Kornhauser Health Sciences Library",
     "modality": "documented",
     "kind": "attribution",
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     "lineage": "who makes it",
     "quote_ok": true,
     "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
    },
    "F39": {
     "source": "B8",
     "locus": "¶5",
     "quote": "He also discusses what’s in scope that he’s tracking (journal articles, conference papers/proceedings, and books/chapters) and out of scope (theses, preprints, blog posts, and other media).",
     "claim": "scope as reported in August 2025: theses out of scope",
     "modality": "attributed",
     "kind": "fact",
     "kernel": false,
     "const": false,
     "lineage": "scope: what is admitted",
     "quote_ok": true,
     "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
    },
    "F40": {
     "source": "B8",
     "locus": "¶6",
     "quote": "772 in all! That includes 633 journal articles, 111 conference papers, and 28 books and chapters.",
     "claim": "772 examples in August 2025: 633 journal articles, 111 conference papers, 28 books and chapters",
     "modality": "documented",
     "kind": "fact",
     "kernel": true,
     "const": false,
     "lineage": "its size",
     "quote_ok": true,
     "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
    },
    "F41": {
     "source": "B8",
     "locus": "¶8",
     "quote": "articles corrected or retracted without such a statement or simply noting that a change occurred without further explanation (which Academ-AI refers to as “stealth corrections”)",
     "claim": "the errata page's second category, 'stealth corrections' (as reported in August 2025)",
     "modality": "attributed",
     "kind": "definition",
     "kernel": false,
     "const": false,
     "lineage": "after publication: corrections, formal and stealth",
     "quote_ok": true,
     "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
    },
    "F42": {
     "source": "B8",
     "locus": "¶9",
     "quote": "Elsevier (which was discussed in my post last year) has 34 examples, which is a lot. But the publisher with the most examples is the Institute of Electrical and Electronics Engineers, with a whopping 90!",
     "claim": "by publisher (August 2025): IEEE 90, Elsevier 34",
     "modality": "documented",
     "kind": "fact",
     "kernel": false,
     "const": false,
     "lineage": "by publisher",
     "quote_ok": true,
     "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
    },
    "F43": {
     "source": "B2",
     "locus": "card snippet",
     "quote": "Core Offering: Academi.AI AI-powered study assistant (Samsung, Google, Adobe, Spotify, Dropbox partners). Features: AI-Powered Chat Tutor (personalized adaptive",
     "claim": "homonym Academi.AI: an AI-powered study assistant with a chat tutor",
     "modality": "stipulation",
     "kind": "definition",
     "kernel": true,
     "const": false,
     "lineage": "homonym: Academi.AI",
     "from": "snippet",
     "quote_ok": true,
     "checked_against": "card snippet in the Capture Registry's cite_list (T1 card 2 (academ-ai-aio-20261029))"
    },
    "F44": {
     "source": "B3",
     "locus": "home page, opening",
     "quote": "Independent premium training for mastering Microsoft Copilot, ChatGPT, OpenAI, Perplexity, Claude, Grok, Ollama Local AI Lab, Codex, APIs, and practical AI workflows.",
     "claim": "homonym AcademAI: independent premium training in AI tools and workflows",
     "modality": "stipulation",
     "kind": "definition",
     "kernel": true,
     "const": false,
     "lineage": "homonym: AcademAI",
     "quote_ok": true,
     "checked_against": "saved text B3.txt (sha256 6e4e7f78892e7b85…), whitespace-normalised"
    },
    "F45": {
     "source": "B3",
     "locus": "home page",
     "quote": "Learn practical AI skills with the free AI Fluency foundation course.",
     "claim": "AcademAI: a free AI Fluency foundation course; membership unlocks tracks, assessments, certificates",
     "modality": "documented",
     "kind": "function",
     "kernel": false,
     "const": false,
     "lineage": "homonym: AcademAI",
     "quote_ok": true,
     "checked_against": "saved text B3.txt (sha256 6e4e7f78892e7b85…), whitespace-normalised"
    },
    "F46": {
     "source": "B3",
     "locus": "home page, footer",
     "quote": "© 2026 AcademAI. Independent educational project.",
     "claim": "AcademAI: an independent educational project",
     "modality": "self-description",
     "kind": "fact",
     "kernel": true,
     "const": false,
     "lineage": "homonym: AcademAI",
     "quote_ok": true,
     "checked_against": "saved text B3.txt (sha256 6e4e7f78892e7b85…), whitespace-normalised"
    },
    "F47": {
     "source": "B4",
     "locus": "card snippet",
     "quote": "Platform: Academiq.ai unified AI education ecosystem with 150+ tools for teachers, students, parents, and institutions.",
     "claim": "homonym Academiq: an AI education ecosystem with 150+ tools",
     "modality": "stipulation",
     "kind": "definition",
     "kernel": true,
     "const": false,
     "lineage": "homonym: Academiq",
     "from": "snippet",
     "quote_ok": true,
     "checked_against": "card snippet in the Capture Registry's cite_list (T1 card 4 (academ-ai-aio-20261029))"
    }
   },
   "field_sources": {
    "B1": "arXiv — [2411.15218] Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing",
    "B2": "Academi.AI — Academi.AI",
    "B3": "academai.app — AcademAI — Premium AI Training Console",
    "B4": "Academiq — AI Education Platform | AI Tools for Teachers & Students – Academiq",
    "B5": "the dataset paper's full text, v2 (PDF, 15 Nov 2025; 24 pages), by the operator — https://arxiv.org/pdf/2411.15218v2",
    "B6": "the entity's own site, home page (301 to https://academ-ai.info/; a Nuxt page whose list of examples loads client-side) — https://www.academ-ai.info/",
    "B7": "the entity's own site, About page (reached from the home page's navigation: 'About') — https://www.academ-ai.info/about",
    "B8": "third-party article (eDiscovery Today, 2025-08-27, 'AI Use in Academic Papers? There's a Site for That Too') — https://ediscoverytoday.com/2025/08/27/ai-use-in-academic-papers-theres-a-site-for-that-too-artificial-intelligence-trends/"
   }
  },
  "P": {
   "title": "Academ-AI",
   "lede": {
    "text": "is Alex Glynn's database of published scholarship with suspected undeclared AI use, found by chatbot phrasing (MMRS record; About): \"Suspected undeclared AI usage in the academic literature\" (its home page).",
    "claims": [
     "MMRS-01",
     "F28",
     "F29"
    ]
   },
   "sections": [
    {
     "icon": "📚",
     "head": "What it is",
     "items": [
      {
       "label": "Definition:",
       "text": "it documents \"suspected instances of AI being used to author research without appropriate declaration\" (About); a database of cases with errata and publishers pages, \"NOT only the dataset paper\" (MMRS record; Capture Registry).",
       "claims": [
        "F29",
        "F1",
        "F11",
        "MMRS-14",
        "MMRS-15",
        "MMRS-29",
        "CAP-1-01"
       ]
      },
      {
       "label": "Maker:",
       "text": "Alex Glynn, Kornhauser Health Sciences Library, Louisville (eDiscovery Today; MMRS record); METRICS, Stanford (the paper).",
       "claims": [
        "F38",
        "F10",
        "F12",
        "MMRS-02",
        "MMRS-13"
       ]
      }
     ]
    },
    {
     "icon": "🔎",
     "head": "How cases enter",
     "items": [
      {
       "label": "Method:",
       "text": "Google Scholar searched with chatbot phrases, results read by hand; reader submissions (the paper; About; MMRS record).",
       "claims": [
        "F13",
        "F14",
        "F31",
        "MMRS-03",
        "MMRS-16"
       ]
      },
      {
       "label": "Evidence kept:",
       "text": "each passage quoted, the phrases highlighted (About; MMRS record).",
       "claims": [
        "F30",
        "MMRS-04",
        "MMRS-23"
       ]
      },
      {
       "label": "Scope, two dates:",
       "text": "theses now in, preprints and blogs out (About, 2026-10-29); theses out in August 2025 (eDiscovery Today; MMRS record); the paper: articles, conference papers, books.",
       "claims": [
        "F32",
        "F39",
        "MMRS-05",
        "MMRS-18",
        "F15"
       ]
      },
      {
       "label": "Metadata:",
       "text": "as published, errors left, \"to highlight publishing oversights\" (About).",
       "claims": [
        "F35"
       ]
      }
     ]
    },
    {
     "icon": "📊",
     "head": "What it finds",
     "items": [
      {
       "label": "Size:",
       "text": "772 in August 2025, current count unchecked (eDiscovery Today; MMRS record); the paper's first 768.",
       "claims": [
        "F40",
        "MMRS-17",
        "MMRS-22",
        "F16",
        "F3",
        "MMRS-08"
       ]
      },
      {
       "label": "Paper:",
       "text": "v1 November 2024, v2 November 2025, IntelliSys 2026; data on Figshare (arXiv).",
       "claims": [
        "F8",
        "F27",
        "F9",
        "F26"
       ]
      },
      {
       "label": "Where:",
       "text": "\"seems to appear\" in journals with higher citation metrics and APCs; one in four past a major publisher's explicit policy; 44% cite a training cutoff (the paper).",
       "claims": [
        "F4",
        "F20",
        "F21",
        "F17"
       ]
      },
      {
       "label": "After:",
       "text": "4.3% altered; formal corrections apart from \"stealth revision\" (the paper; About; eDiscovery Today; MMRS record).",
       "claims": [
        "F5",
        "F18",
        "F19",
        "F36",
        "F41",
        "MMRS-06",
        "MMRS-19"
       ]
      },
      {
       "label": "Publishers:",
       "text": "IEEE 90, Elsevier 34, August 2025 (eDiscovery Today; MMRS record).",
       "claims": [
        "F42",
        "MMRS-07"
       ]
      },
      {
       "label": "Norm:",
       "text": "declare; undeclared use \"a serious problem\"; publishers must enforce (the paper; About).",
       "claims": [
        "F2",
        "F34",
        "F7"
       ]
      }
     ]
    },
    {
     "icon": "⚠️",
     "head": "What its selection leaves out",
     "items": [
      {
       "label": "Unit:",
       "text": "\"a detectable trace\" (MMRS record); the 768 \"likely represent a small fraction\" (the paper; About).",
       "claims": [
        "MMRS-09",
        "MMRS-10",
        "F6",
        "F22",
        "F33",
        "F23"
       ]
      },
      {
       "label": "Coverage:",
       "text": "manual, English-language, mainly Google Scholar (the paper).",
       "claims": [
        "F24",
        "F25"
       ]
      }
     ]
    },
    {
     "icon": "🧭",
     "head": "Place in the field",
     "items": [
      {
       "label": "Neighbours:",
       "text": "beside the hallucination-cases tracker (eDiscovery Today); with the Problematic Paper Screener, the machine \"the uncredited author\", reading \"by phrase where the screener reads by detector\" (MMRS record).",
       "claims": [
        "F37",
        "MMRS-11",
        "MMRS-12"
       ]
      },
      {
       "label": "Lifecycle:",
       "text": "active, kept apart from its observation: blocked to the record's fetcher, 2026-10-28 (MMRS record).",
       "claims": [
        "MMRS-20",
        "MMRS-21",
        "MMRS-31"
       ]
      }
     ]
    },
    {
     "icon": "🔀",
     "head": "One name, four entities",
     "items": [
      {
       "label": "Homonyms:",
       "text": "AcademAI, a training console (academai.app); Academi.AI, a study tutor; Academiq, an education platform (their cards): distinct entities (MMRS record).",
       "claims": [
        "F44",
        "F45",
        "F46",
        "F43",
        "F47",
        "MMRS-25",
        "MMRS-26",
        "MMRS-27",
        "MMRS-28",
        "MMRS-24"
       ]
      },
      {
       "label": "Reception:",
       "text": "the Overview gave the paper as the entity, without Glynn, among the homonyms (Capture Registry, 2026-10-29).",
       "claims": [
        "CAP-1-02",
        "CAP-1-04",
        "CAP-1-05",
        "CAP-1-06",
        "CAP-1-07",
        "CAP-1-08",
        "CAP-1-09",
        "CAP-1-03",
        "MMRS-30"
       ]
      }
     ]
    }
   ],
   "open": [
    {
     "question": "Are theses in scope?",
     "rests_on": [
      "F32",
      "F39",
      "MMRS-05",
      "MMRS-18",
      "F15"
     ],
     "modality": "stipulation on the About page as read 2026-10-29 ('dissertations/theses'); attributed in eDiscovery Today's report of the About page, 27 August 2025 (theses out of scope), which the MMRS record follows; the paper's criteria (v2) name journal articles, conference papers and books",
     "would_resolve": "a dated copy of the About page between August 2025 and October 2026, or a thesis entry on the site with its date of listing"
    },
    {
     "question": "What is the current count?",
     "rests_on": [
      "F40",
      "MMRS-17",
      "MMRS-22",
      "F16"
     ],
     "modality": "documented at two dates (772 in August 2025, third-party; 768 in the paper's census); the record leaves the current count unchecked; the served HTML of 2026-10-29 carries no count (the list loads client-side, B6)",
     "would_resolve": "the site's own count as served to a reader, dated, or the Figshare dump's next version"
    },
    {
     "question": "Does the site refuse Google?",
     "rests_on": [
      "CAP-1-02",
      "CAP-1-08",
      "MMRS-21"
     ],
     "modality": "the registry's 'may refuse Google's' is stated as a possibility; the robots.txt saved 2026-10-29 (B6-robots.txt) allows 'User-agent: *' with 'search=yes,ai-train=no,use=reference' and disallows Google-Extended, GoogleOther, Google-CloudVertexBot and Google-Agent by name, not Googlebot; the shell fetch was answered 200",
     "would_resolve": "a later capture at 'academ-ai' whose cards include academ-ai.info, or the site's appearance in Google's index for its own name"
    },
    {
     "question": "How much undeclared AI does the trace miss?",
     "rests_on": [
      "F6",
      "F22",
      "F23",
      "F33",
      "MMRS-09",
      "MMRS-10"
     ],
     "modality": "self-description and hypothesis (the paper: 'likely', 'may be undetectable'; the About page: 'impossible to say'); the record: a stipulation of the unit",
     "would_resolve": "a study that measures lightly edited AI text in a sample of the literature against Academ-AI's phrase-based finds"
    },
    {
     "question": "'Stealth revision' or 'stealth corrections'?",
     "rests_on": [
      "F36",
      "F41",
      "F19",
      "MMRS-06",
      "MMRS-24"
     ],
     "modality": "stipulation (the About page's label, 2026-10-29: 'stealth revision'); attributed (eDiscovery Today, 2025: 'stealth corrections'), which the record carries; the paper counts stealth corrections and stealth retractions",
     "would_resolve": "the errata page as served, dated"
    },
    {
     "question": "Opacity: two homonym cards by snippet only",
     "rests_on": [
      "F43",
      "F47"
     ],
     "modality": "snippet (Academi.AI: connection reset; Academiq: HTTP 406 from Mod_Security)",
     "would_resolve": "the two pages fetched verbatim"
    }
   ],
   "rail": [
    {
     "lineage": "what it is",
     "claims": [
      "F29",
      "F28",
      "F1",
      "F11",
      "MMRS-01",
      "MMRS-14",
      "MMRS-15",
      "MMRS-29",
      "CAP-1-01"
     ],
     "sources": [
      "B7",
      "B6",
      "B1",
      "B5",
      "MMRS",
      "CAP"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "who makes it",
     "claims": [
      "F10",
      "F12",
      "F38",
      "MMRS-02",
      "MMRS-13",
      "CAP-1-04"
     ],
     "sources": [
      "B1",
      "B5",
      "B8",
      "MMRS",
      "CAP"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "how cases enter",
     "claims": [
      "F13",
      "F14",
      "F30",
      "F31",
      "MMRS-03",
      "MMRS-04",
      "MMRS-16",
      "MMRS-23"
     ],
     "sources": [
      "B5",
      "B7",
      "MMRS"
     ],
     "earliest": "B5"
    },
    {
     "lineage": "scope: what is admitted",
     "claims": [
      "F15",
      "F32",
      "F39",
      "MMRS-05",
      "MMRS-18"
     ],
     "sources": [
      "B5",
      "B7",
      "B8",
      "MMRS"
     ],
     "earliest": "B8"
    },
    {
     "lineage": "metadata as published",
     "claims": [
      "F35"
     ],
     "sources": [
      "B7"
     ],
     "earliest": "B7"
    },
    {
     "lineage": "its size",
     "claims": [
      "F40",
      "F16",
      "MMRS-17",
      "MMRS-22"
     ],
     "sources": [
      "B8",
      "B5",
      "MMRS"
     ],
     "earliest": "B8"
    },
    {
     "lineage": "the dataset paper: the first 768 cases",
     "claims": [
      "F3",
      "MMRS-08"
     ],
     "sources": [
      "B1",
      "MMRS"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the dataset paper: its versions and venue",
     "claims": [
      "F8",
      "F27",
      "F9"
     ],
     "sources": [
      "B1",
      "B5"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the tells",
     "claims": [
      "F17"
     ],
     "sources": [
      "B5"
     ],
     "earliest": "B5"
    },
    {
     "lineage": "where it appears: higher APCs and citation metrics",
     "claims": [
      "F4",
      "F20",
      "F21",
      "MMRS-08"
     ],
     "sources": [
      "B1",
      "B5",
      "MMRS"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "after publication: corrections, formal and stealth",
     "claims": [
      "F5",
      "F18",
      "F19",
      "F36",
      "F41",
      "MMRS-06",
      "MMRS-19"
     ],
     "sources": [
      "B1",
      "B5",
      "B7",
      "B8",
      "MMRS"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "by publisher",
     "claims": [
      "F42",
      "MMRS-07"
     ],
     "sources": [
      "B8",
      "MMRS"
     ],
     "earliest": "B8"
    },
    {
     "lineage": "the norm: declaration",
     "claims": [
      "F2",
      "F34"
     ],
     "sources": [
      "B1",
      "B7"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the call: enforcement",
     "claims": [
      "F7"
     ],
     "sources": [
      "B1"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the selection's limit: the detectable trace",
     "claims": [
      "F6",
      "F22",
      "F23",
      "F33",
      "MMRS-09",
      "MMRS-10"
     ],
     "sources": [
      "B1",
      "B5",
      "B7",
      "MMRS"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the selection's limit: coverage",
     "claims": [
      "F24",
      "F25"
     ],
     "sources": [
      "B5"
     ],
     "earliest": "B5"
    },
    {
     "lineage": "the data dump",
     "claims": [
      "F26"
     ],
     "sources": [
      "B5"
     ],
     "earliest": "B5"
    },
    {
     "lineage": "its neighbours",
     "claims": [
      "F37"
     ],
     "sources": [
      "B8"
     ],
     "earliest": "B8"
    },
    {
     "lineage": "place in the field: machine composition entering the record unattributed",
     "claims": [
      "MMRS-11",
      "MMRS-12"
     ],
     "sources": [
      "MMRS"
     ],
     "earliest": "MMRS"
    },
    {
     "lineage": "the lifecycle and the observation, kept apart",
     "claims": [
      "MMRS-20",
      "MMRS-21",
      "MMRS-31"
     ],
     "sources": [
      "MMRS"
     ],
     "earliest": "MMRS"
    },
    {
     "lineage": "the name and its homonyms",
     "claims": [
      "MMRS-28",
      "MMRS-24",
      "CAP-1-05",
      "CAP-1-06",
      "CAP-1-09"
     ],
     "sources": [
      "MMRS",
      "CAP"
     ],
     "earliest": "MMRS"
    },
    {
     "lineage": "homonym: AcademAI",
     "claims": [
      "F44",
      "F45",
      "F46",
      "MMRS-25"
     ],
     "sources": [
      "B3",
      "MMRS"
     ],
     "earliest": "B3"
    },
    {
     "lineage": "homonym: Academi.AI",
     "claims": [
      "F43",
      "MMRS-26"
     ],
     "sources": [
      "B2",
      "MMRS"
     ],
     "earliest": "B2"
    },
    {
     "lineage": "homonym: Academiq",
     "claims": [
      "F47",
      "MMRS-27"
     ],
     "sources": [
      "B4",
      "MMRS"
     ],
     "earliest": "B4"
    },
    {
     "lineage": "its reception: the paper composed as the entity",
     "claims": [
      "CAP-1-02",
      "CAP-1-03",
      "CAP-1-07",
      "CAP-1-08",
      "MMRS-30"
     ],
     "sources": [
      "CAP",
      "MMRS"
     ],
     "earliest": "CAP"
    }
   ],
   "genre": "the compression: AIO's interaction grammar (lede, headed clusters, bolded labels, card rail), composed from the v0.9 plan in one claim space; it faces on the card, with AIO's transcript a record within it",
   "status": "composed 2026-10-29 from the v0.9 knowledge object; not frozen",
   "body_words": 350,
   "entity": "Composed under v0.9, equal case (ρ = extends): KO_B sentences surviving 0 of 9; every field claim carried."
  },
  "P_B": {
   "title": "Academ-AI",
   "arm": "L_F(B), the field's ontology",
   "genre": "the same grammar, composed from the field alone, in the field's ontology",
   "status": "composed 2026-10-29 from the field ledger alone; not frozen",
   "lede": {
    "text": "documents examples of suspected undeclared AI usage in the academic literature, found by chatbot verbiage, a repository Alex Glynn maintains (arXiv:2411.15218; academ-ai.info/about).",
    "claims": [
     "F1",
     "F11",
     "F29"
    ]
   },
   "sections": [
    {
     "icon": "🔎",
     "head": "How it works",
     "items": [
      {
       "label": "Method:",
       "text": "Google Scholar searched with chatbot phrases, results read by hand; phrases highlighted (the paper; About).",
       "claims": [
        "F13",
        "F14",
        "F30"
       ]
      },
      {
       "label": "Scope:",
       "text": "articles, conference papers, books, theses now (About); theses out in 2025 (eDiscovery Today).",
       "claims": [
        "F32",
        "F39",
        "F15"
       ]
      }
     ]
    },
    {
     "icon": "📊",
     "head": "What it finds",
     "items": [
      {
       "label": "Size:",
       "text": "772 in August 2025 (eDiscovery Today); the paper's 768.",
       "claims": [
        "F40",
        "F16",
        "F3"
       ]
      },
      {
       "label": "Where:",
       "text": "journals with higher citation metrics and APCs (the paper).",
       "claims": [
        "F4",
        "F21"
       ]
      },
      {
       "label": "After:",
       "text": "few corrected; \"stealth revision\" (the paper; About).",
       "claims": [
        "F5",
        "F18",
        "F36"
       ]
      }
     ]
    },
    {
     "icon": "⚠️",
     "head": "Limits",
     "items": [
      {
       "label": "Trace:",
       "text": "a small fraction; much may be undetectable (the paper).",
       "claims": [
        "F6",
        "F22",
        "F24"
       ]
      }
     ]
    },
    {
     "icon": "🔀",
     "head": "Similar names",
     "items": [
      {
       "label": "Products:",
       "text": "AcademAI, Academi.AI, Academiq (their cards).",
       "claims": [
        "F44",
        "F43",
        "F47"
       ]
      }
     ]
    }
   ],
   "open": [
    {
     "question": "Are theses in scope?",
     "rests_on": [
      "F32",
      "F39"
     ],
     "modality": "stipulation (2026) against attributed (2025)",
     "would_resolve": "a dated About page"
    },
    {
     "question": "Opacity",
     "rests_on": [
      "F43",
      "F47"
     ],
     "modality": "two homonym cards by snippet only",
     "would_resolve": "the pages fetched"
    }
   ],
   "entity": "The field's entity: a database (repository) of suspected undeclared AI use in published scholarship, Alex Glynn's at academ-ai.info, with its dataset paper (arXiv:2411.15218); three education products with similar names on the same rail.",
   "rail": [
    {
     "lineage": "what it is",
     "claims": [
      "F29",
      "F28",
      "F1",
      "F11"
     ],
     "sources": [
      "B7",
      "B6",
      "B1",
      "B5"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "who makes it",
     "claims": [
      "F10",
      "F12",
      "F38"
     ],
     "sources": [
      "B1",
      "B5",
      "B8"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "how cases enter",
     "claims": [
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      "F31"
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     "sources": [
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     "earliest": "B5"
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    {
     "lineage": "scope: what is admitted",
     "claims": [
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     "sources": [
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     "earliest": "B8"
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    {
     "lineage": "metadata as published",
     "claims": [
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     "sources": [
      "B7"
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     "earliest": "B7"
    },
    {
     "lineage": "its size",
     "claims": [
      "F40",
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     ],
     "sources": [
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     ],
     "earliest": "B8"
    },
    {
     "lineage": "the dataset paper: the first 768 cases",
     "claims": [
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     "sources": [
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     "earliest": "B1"
    },
    {
     "lineage": "the dataset paper: its versions and venue",
     "claims": [
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      "F9"
     ],
     "sources": [
      "B1",
      "B5"
     ],
     "earliest": "B1"
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    {
     "lineage": "the tells",
     "claims": [
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     ],
     "sources": [
      "B5"
     ],
     "earliest": "B5"
    },
    {
     "lineage": "where it appears: higher APCs and citation metrics",
     "claims": [
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      "F20",
      "F21"
     ],
     "sources": [
      "B1",
      "B5"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "after publication: corrections, formal and stealth",
     "claims": [
      "F5",
      "F18",
      "F19",
      "F36",
      "F41"
     ],
     "sources": [
      "B1",
      "B5",
      "B7",
      "B8"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "by publisher",
     "claims": [
      "F42"
     ],
     "sources": [
      "B8"
     ],
     "earliest": "B8"
    },
    {
     "lineage": "the norm: declaration",
     "claims": [
      "F2",
      "F34"
     ],
     "sources": [
      "B1",
      "B7"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the call: enforcement",
     "claims": [
      "F7"
     ],
     "sources": [
      "B1"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the selection's limit: the detectable trace",
     "claims": [
      "F6",
      "F22",
      "F23",
      "F33"
     ],
     "sources": [
      "B1",
      "B5",
      "B7"
     ],
     "earliest": "B1"
    },
    {
     "lineage": "the selection's limit: coverage",
     "claims": [
      "F24",
      "F25"
     ],
     "sources": [
      "B5"
     ],
     "earliest": "B5"
    },
    {
     "lineage": "the data dump",
     "claims": [
      "F26"
     ],
     "sources": [
      "B5"
     ],
     "earliest": "B5"
    },
    {
     "lineage": "its neighbours",
     "claims": [
      "F37"
     ],
     "sources": [
      "B8"
     ],
     "earliest": "B8"
    },
    {
     "lineage": "homonym: AcademAI",
     "claims": [
      "F44",
      "F45",
      "F46"
     ],
     "sources": [
      "B3"
     ],
     "earliest": "B3"
    },
    {
     "lineage": "homonym: Academi.AI",
     "claims": [
      "F43"
     ],
     "sources": [
      "B2"
     ],
     "earliest": "B2"
    },
    {
     "lineage": "homonym: Academiq",
     "claims": [
      "F47"
     ],
     "sources": [
      "B4"
     ],
     "earliest": "B4"
    }
   ],
   "body_words": 101
  },
  "KO_B": {
   "title": "Academ-AI",
   "arm": "L_F(B), the field's ontology",
   "genre": "the field alone",
   "status": "composed 2026-10-29; not frozen",
   "sentences": [
    {
     "n": 1,
     "para": 0,
     "sense": "what it is",
     "text": "Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily by the idiosyncratic verbiage of LLM-based chatbots, a repository Alex Glynn maintains at academ-ai.info (arXiv:2411.15218), which documents \"suspected instances of AI being used to author research without appropriate declaration\" (academ-ai.info/about).",
     "claims": [
      "F1",
      "F11",
      "F10",
      "F28",
      "F29"
     ]
    },
    {
     "n": 2,
     "para": 0,
     "sense": "who makes it",
     "text": "Glynn is a research literacy and communications instructor at the Kornhauser Health Sciences Library, University of Louisville (eDiscovery Today), also affiliated with METRICS at Stanford (arXiv:2411.15218).",
     "claims": [
      "F38",
      "F12"
     ]
    },
    {
     "n": 3,
     "para": 1,
     "sense": "how cases enter",
     "text": "Cases are found by querying Google Scholar with phrases characteristic of AI text and examined by hand (arXiv:2411.15218), or sent in by readers, and each is quoted with the suspicious phrases highlighted; the scope is journal articles, conference papers, books and, now, theses (academ-ai.info/about), theses having been out of scope in August 2025 (eDiscovery Today), and publications are cited with the publisher's metadata as published (academ-ai.info/about).",
     "claims": [
      "F13",
      "F14",
      "F31",
      "F30",
      "F15",
      "F32",
      "F39",
      "F35"
     ]
    },
    {
     "n": 4,
     "para": 2,
     "sense": "its size",
     "text": "It held 772 examples in August 2025, 633 journal articles, 111 conference papers and 28 books and chapters (eDiscovery Today); the paper, posted in November 2024 and revised in November 2025 for IntelliSys 2026, analyses the first 768 (arXiv:2411.15218).",
     "claims": [
      "F40",
      "F8",
      "F27",
      "F9",
      "F16",
      "F3"
     ]
    },
    {
     "n": 5,
     "para": 2,
     "sense": "findings",
     "text": "The paper finds the problem widespread among highly respected publishers, undeclared AI seeming to appear in journals with higher citation metrics and APCs, about one in four cases past a major publisher's explicit policy, and 44% citing a training cutoff (arXiv:2411.15218).",
     "claims": [
      "F3",
      "F4",
      "F20",
      "F21",
      "F17"
     ]
    },
    {
     "n": 6,
     "para": 3,
     "sense": "after publication",
     "text": "Few cases are corrected, 4.3% altered, formal alteration being best practice and stealth alteration compromising integrity (arXiv:2411.15218); the site marks silent corrections \"stealth revision\" (academ-ai.info/about), and its errata and publishers pages counted IEEE 90 and Elsevier 34 in August 2025 (eDiscovery Today).",
     "claims": [
      "F5",
      "F18",
      "F19",
      "F36",
      "F41",
      "F42"
     ]
    },
    {
     "n": 7,
     "para": 4,
     "sense": "the norm and its limits",
     "text": "Declaration is the publishing community's norm, failure to declare \"a serious problem\", and publishers must enforce their policies where use is detectable (arXiv:2411.15218; academ-ai.info/about); the 768 likely represent a small fraction, much undeclared AI being undetectable, the coverage manual and English-language (arXiv:2411.15218; academ-ai.info/about).",
     "claims": [
      "F2",
      "F34",
      "F7",
      "F6",
      "F22",
      "F23",
      "F33",
      "F24",
      "F25"
     ]
    },
    {
     "n": 8,
     "para": 4,
     "sense": "data and neighbours",
     "text": "Its data are on Figshare (arXiv:2411.15218), and eDiscovery Today sets it beside a tracker of AI hallucinations in case filings (eDiscovery Today).",
     "claims": [
      "F26",
      "F37"
     ]
    },
    {
     "n": 9,
     "para": 5,
     "sense": "homonyms",
     "text": "The name sounds like three education products: AcademAI, an independent AI training project (academai.app); Academi.AI, an AI study assistant; and Academiq, an AI education ecosystem (their cards).",
     "claims": [
      "F44",
      "F45",
      "F46",
      "F43",
      "F47"
     ]
    }
   ]
  },
  "L_B": {
   "words": 384,
   "text": "Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily by the idiosyncratic verbiage of LLM-based chatbots, a repository Alex Glynn maintains at academ-ai.info (arXiv:2411.15218), which documents \"suspected instances of AI being used to author research without appropriate declaration\" (academ-ai.info/about). Glynn is a research literacy and communications instructor at the Kornhauser Health Sciences Library, University of Louisville (eDiscovery Today), also affiliated with METRICS at Stanford (arXiv:2411.15218). Cases are found by querying Google Scholar with phrases characteristic of AI text and examined by hand (arXiv:2411.15218), or sent in by readers, and each is quoted with the suspicious phrases highlighted; the scope is journal articles, conference papers, books and, now, theses (academ-ai.info/about), theses having been out of scope in August 2025 (eDiscovery Today), and publications are cited with the publisher's metadata as published (academ-ai.info/about). It held 772 examples in August 2025, 633 journal articles, 111 conference papers and 28 books and chapters (eDiscovery Today); the paper, posted in November 2024 and revised in November 2025 for IntelliSys 2026, analyses the first 768 (arXiv:2411.15218). The paper finds the problem widespread among highly respected publishers, undeclared AI seeming to appear in journals with higher citation metrics and APCs, about one in four cases past a major publisher's explicit policy, and 44% citing a training cutoff (arXiv:2411.15218). Few cases are corrected, 4.3% altered, formal alteration being best practice and stealth alteration compromising integrity (arXiv:2411.15218); the site marks silent corrections \"stealth revision\" (academ-ai.info/about), and its errata and publishers pages counted IEEE 90 and Elsevier 34 in August 2025 (eDiscovery Today). Declaration is the publishing community's norm, failure to declare \"a serious problem\", and publishers must enforce their policies where use is detectable (arXiv:2411.15218; academ-ai.info/about); the 768 likely represent a small fraction, much undeclared AI being undetectable, the coverage manual and English-language (arXiv:2411.15218; academ-ai.info/about). Its data are on Figshare (arXiv:2411.15218), and eDiscovery Today sets it beside a tracker of AI hallucinations in case filings (eDiscovery Today). The name sounds like three education products: AcademAI, an independent AI training project (academai.app); Academi.AI, an AI study assistant; and Academiq, an AI education ecosystem (their cards).",
   "note": "L_F(B) is realized as the field arm's expansion (objects.KO_B): the field alone, cards, own pages and the MMRS source."
  },
  "L_BA": {
   "words": 1415,
   "text": "Academ-AI is Alex Glynn's database of published scholarship with suspected undeclared use of AI, identified by chatbot phrasing and quoted with the phrases highlighted (MMRS record), and its own About page says it \"documents the adverse effects of artificial intelligence (AI) in academia, particularly suspected instances of AI being used to author research without appropriate declaration\" (academ-ai.info/about). Its home page heads it \"Suspected undeclared AI usage in the academic literature\" (academ-ai.info), and in his dataset paper Glynn writes \"I maintain a repository of examples known as Academ-AI\", which \"documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots\" (Glynn, arXiv:2411.15218). The MMRS record keeps the database apart from the paper, \"NOT only the dataset paper (arXiv:2411.15218): the paper analyses the database's first 768 cases\", and records its kind as database and its role as monitoring (MMRS record); the Capture Registry gives it as \"a database of cases, with an errata page and a publishers page\" (Capture Registry, 2026-10-29). Alex Glynn made it: eDiscovery Today names him \"Research Literacy and Communications Instructor at the Kornhauser Health Sciences Library, University of Louisville\" (eDiscovery Today, 27 August 2025), the MMRS record gives him so, as a research literacy instructor, on that third-party basis (MMRS record), and the paper, \"Authors: Alex Glynn\", adds a second affiliation, the Meta-Research Innovation Center at Stanford (METRICS) (arXiv:2411.15218). An entry is a published work whose text carries phrasing that strongly suggests a chatbot wrote it, without any disclosure (MMRS record); the site's articles \"have been identified based on phrases that strongly suggest AI use (highlighted in each quoted passage)\" (academ-ai.info/about), and the record holds that it preserves the original outputs, \"quoted passages with the phrases highlighted\" (MMRS record). In the paper's method, \"Phrases characteristic of AI-generated text were used to query Google Scholar\" and \"Each result was manually examined and included or excluded according to the following criteria\" (arXiv:2411.15218); readers may also send a suspected case with its citation and passage (academ-ai.info/about). The scope is stated two ways at two dates: the About page now documents \"journal articles, conference papers/proceedings, books/chapters, and dissertations/theses only\", with preprints, blog posts and other media out of scope (academ-ai.info/about, read 2026-10-29), while eDiscovery Today reported theses out of scope in August 2025 (eDiscovery Today) and the MMRS record follows that report, \"theses, preprints and blog posts are excluded\" (MMRS record); the paper excluded \"Any medium other than journal articles, conference papers, and books\" (arXiv:2411.15218). It cites each publication with the publisher's metadata, even where implausible, and leaves the errors standing: \"Since the purpose of Academ-AI is to highlight publishing oversights, no attempt will be made to resolve these errors\" (academ-ai.info/about). Its size is dated: 772 examples in August 2025, 633 journal articles, 111 conference papers and 28 books and chapters (eDiscovery Today; MMRS record), and the record leaves \"the current count (the site refuses our fetcher)\" unchecked (MMRS record). The dataset paper was posted on 20 November 2024 as \"Suspected Undeclared Use of Artificial Intelligence in the Academic Literature: An Analysis of the Academ-AI Dataset\" and revised on 15 November 2025, with a journal reference in Springer's Lecture Notes in Networks and Systems (IntelliSys 2026); its dataset is \"768 published documents: 633 journal articles, 107 conference papers, and 28 book chapters\" (arXiv:2411.15218), which the MMRS record reads as the database's first 768 cases (MMRS record). The paper finds the problem \"widespread, penetrating the journals, conference proceedings, and textbooks of highly respected publishers\", and \"44% of the documents in the Academ-AI dataset make a similar reference to their training cutoff\" (arXiv:2411.15218). Undeclared AI \"seems to appear in journals with higher citation metrics and higher article processing charges (APCs)\"; \"approximately one in four of these evaded detection by a major publisher with an explicit policy against such content\", and the journals with the oversights \"are paradoxically paid more for editorial processing and achieve greater academic impact\" (arXiv:2411.15218), which the MMRS record carries as undeclared use found more often in journals with higher citation metrics and higher article charges (MMRS record). After publication \"An extremely small minority of cases are corrected\", the corrections often insufficient: 33 documents (4.3%) were altered, and \"Formal alteration is best practice, while stealth alteration compromises transparency and scientific integrity\" (arXiv:2411.15218); the site marks silent publisher corrections \"stealth revision\" (academ-ai.info/about), and its errata page, as eDiscovery Today reported it and the MMRS record gives it, separates corrections with formal explanations from \"stealth corrections\" (eDiscovery Today; MMRS record). A publishers page counts cases by publisher: in August 2025 IEEE led with 90 and Elsevier followed with 34 (eDiscovery Today; MMRS record). The norm it holds publications to is the publishing community's: \"such usage must be declared in the published article\" (arXiv:2411.15218); use \"should not be disqualifying in and of itself, but failure to declare it should be considered a serious problem\" (academ-ai.info/about), and \"Publishers must enforce their policies against undeclared AI usage in cases that are detectable\" (arXiv:2411.15218). Its unit is \"a detectable trace\" (MMRS record): the examples were identifiable by turns of phrase \"only reproduced in a manuscript when authors copy text directly from an AI system with little to no scrutiny\", and the 768 \"likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable\" (arXiv:2411.15218), which the record gives as much undeclared use leaving no phrase behind (MMRS record). The site says \"it is impossible to say just how extensive the failure to report AI usage is\" (academ-ai.info/about); the paper holds, as a hypothesis, that lightly edited AI text may be abundant and undetectable, and that this \"should not deter us from studying and contending with the examples that are observable\", and it states its coverage: manual search, data that \"may not be fully up to date or representative\", and \"English-language literature indexed in online databases accessible to the author, principally Google Scholar\" (arXiv:2411.15218). Its data are deposited as \"Academ-AI data dump\" on Figshare (10.6084/m9.figshare.29650481.v2, 2025) (arXiv:2411.15218). eDiscovery Today sets it beside the site that tracks AI hallucinations in case filings, \"There’s also one tracking AI use in academic papers\" (eDiscovery Today), and the MMRS record places it with the Problematic Paper Screener as a record of machine composition entering the scholarly record unattributed, where \"the machine is the uncredited author, and the publisher is where the reception happens\", the neighbour that \"reads by phrase where the screener reads by detector\" (MMRS record). Its lifecycle is active, held apart from the observation that saw it, by the record's rule that \"lifecycle is kept apart from the observation that saw it\": on 2026-10-28 the record's fetch was \"blocked (robots.txt disallows our fetcher)\" (MMRS record). Three education products share the sound of the name and are distinct entities, \"NOT AcademAI, Academi.AI or Academiq (education products)\" (MMRS record): AcademAI (academai.app), \"Independent premium training for mastering Microsoft Copilot, ChatGPT, OpenAI, Perplexity, Claude, Grok, Ollama Local AI Lab, Codex, APIs, and practical AI workflows\", with \"the free AI Fluency foundation course\", an \"Independent educational project\" (academai.app); Academi.AI, an \"AI-powered study assistant\" with a chat tutor (its card); Academiq, a \"unified AI education ecosystem with 150+ tools for teachers, students, parents, and institutions\" (its card); the record glosses them as a training console, a study tutor and an education platform (MMRS record). The Capture Registry observed the three as \"the name's collision neighbours as the composition layer holds them\", which the record had not had, and the record took them into its disambiguation, where the entity itself is \"Alex Glynn, undeclared AI use, journal articles, errata, stealth corrections\" (Capture Registry, 2026-10-29; MMRS record). At the address 'academ-ai' Google's AI Overview gave the paper as the entity, \"a research study and framework\", the site not among its cards, and \"Alex Glynn is not named\"; the registry reads the composition as made \"from the paper written about it, without its maker\", among three commercial homonyms, at a name its own site does not serve to crawlers, a finding checked against the MMRS record and the entity's own pages (Capture Registry, 2026-10-29; MMRS record).",
   "rail": [],
   "note": "L(B ∪ A), the equal case (v0.9 §0.4), realized as the knowledge object (objects.KO); its rail is the compression's, by lineage."
  }
 },
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   "card": "arXiv — [2411.15218] Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing",
   "fetched": "fetched 2026-10-29 at 05:06:06 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 4f4928f94cb7dd68875693607c4574b2b1b0fa72eaa842de2b87d6ddd2b94964, text sha256 5504faf64b4ef0c813c0e2c19587b7d2ca057f0237276f28ca566cf01eaca474",
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   "card": "Academi.AI — Academi.AI",
   "fetched": "unreachable: https://academi.ai/ at 05:07:53, 05:08:40 UTC and https://www.academi.ai/ at 05:08:29 UTC, curl -sSL -m 30 -A 'Mozilla/5.0', each 'Recv failure: Connection reset by peer' (no HTTP response); WebSearch for the card's title and snippet found only third-party listings (ai.google.dev's Gemini API competition page 'Academi.AI: AI Study Assistant', an App Store listing 'Academi AI - Study & Exam Tutor'), not the card's page; the snippet stands as its only text",
   "origin": "card"
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  {
   "id": "B3",
   "card": "academai.app — AcademAI — Premium AI Training Console",
   "fetched": "fetched 2026-10-29 at 05:09:02 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 83c4140f8819509c4175e98473686a9f79339803b15f81caa46cd39ca40dd913, text sha256 6e4e7f78892e7b85b0d06b1541de0b9d4889852fc2169a3cfcdcd0eae06062e1 (a first fetch at 05:08:03 UTC answered the same title)",
   "origin": "card"
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  {
   "id": "B4",
   "card": "Academiq — AI Education Platform | AI Tools for Teachers & Students – Academiq",
   "fetched": "unreachable: https://academiq.ai/ answered connection reset at 05:08:04 UTC and HTTP 406 at 05:08:50 UTC; https://www.academiq.ai/ answered HTTP 406 'Not Acceptable! ... generated by Mod_Security' at 05:08:07 and 05:09:02 UTC (saved: B4-406.html, sha256 80a265bed528211aa708dcd58f7a95db36eeb7f873c6fe4ddab0b3a1dc0973a4); curl -sSL -m 30 -A 'Mozilla/5.0'; the snippet stands as its only text",
   "origin": "card"
  },
  {
   "id": "B5",
   "card": "the dataset paper's full text, v2 (PDF, 15 Nov 2025; 24 pages), by the operator — https://arxiv.org/pdf/2411.15218v2",
   "fetched": "fetched 2026-10-29 at 05:07:05 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 1d063c0a3ec8619289b129db4ac36a221abc0779e6f078311f14a42a35f092ab, text sha256 ba37182f9263962092537a6271cafa18b39aad6f9cdf70f09233ddeb8cf77ce3 (B5.txt by pdftotext, default mode). Tried first: the HTML version https://arxiv.org/html/2411.15218 (v2) at 05:06:06 UTC, HTTP 200 but an unbuilt shell with no paper text (B5-html-v2-unbuilt.html, sha256 5daab3908fc2d72bdcfb5802211e57713ecef405cdf4c431412dda0a7ebb1fd2); https://arxiv.org/html/2411.15218v1 at 05:06:45 UTC, HTTP 200, the v1 text (first 500 examples; B5-html-v1.html, sha256 e171c4a4eb44eb58b09ac5862d2da913e0b04868268ba2d6b57f6372e01558af), saved and not ledgered, since the record and the card cite v2",
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  {
   "id": "B6",
   "card": "the entity's own site, home page (301 to https://academ-ai.info/; a Nuxt page whose list of examples loads client-side) — https://www.academ-ai.info/",
   "fetched": "fetched 2026-10-29 at 05:06:04 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 68d32fe5488719b025749aed2fda708e932e355f203782974c3af2a3869a34ba, text sha256 2830daf183daabbbdce78f776762f0ba9bd1c18107acb3b8e45b614376b2430c after HTTP 301 from www to the bare domain (B6.headers.txt). The page served to the shell carries the site's heading and navigation; its examples are not in the served HTML. robots.txt fetched at 05:06:05 UTC, HTTP 200 (B6-robots.txt, sha256 0563ce67fbc4e4d58b49682897c6d74c272618c27503668fb7cbb32ee9f2ea36): 'User-agent: *' with 'Content-Signal: search=yes,ai-train=no,use=reference' and 'Allow: /'; Disallow for named crawlers and agents, among them ClaudeBot, Claude-User, Claude-Web, anthropic-ai, GPTBot, ChatGPT-User, Google-Extended, GoogleOther, Google-CloudVertexBot, Google-Agent, Perplexity-User; Googlebot is not named",
   "origin": "own_page"
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  {
   "id": "B7",
   "card": "the entity's own site, About page (reached from the home page's navigation: 'About') — https://www.academ-ai.info/about",
   "fetched": "fetched 2026-10-29 at 05:06:45 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 4f20561a8b120e3a9645eef152cdd3349e9406495bee3f060a0c2c5e5fbf0188, text sha256 2bd6b548492bc40c0b1a424d3bbc0c0c1d9318631d8b22bff2fae7317afc5aae (after 301 to https://academ-ai.info/about)",
   "origin": "own_page"
  },
  {
   "id": "B8",
   "card": "third-party article (eDiscovery Today, 2025-08-27, 'AI Use in Academic Papers? There's a Site for That Too') — https://ediscoverytoday.com/2025/08/27/ai-use-in-academic-papers-theres-a-site-for-that-too-artificial-intelligence-trends/",
   "fetched": "fetched 2026-10-29 at 05:06:07 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 963b9e649ee988528981c1eaed82bdd90ced60a58099325cfaa225f9737c37ab, text sha256 de1f498bbf49a4c539bfa6ccd9fa57609b6cb8299644c39c483334d2d5f3e9a9",
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 "field_claims": {
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   "locus": "Abstract",
   "quote": "Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots.",
   "claim": "what it is: a documentation of suspected undeclared AI use in the literature, discernible mainly by chatbot verbiage",
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  },
  "F2": {
   "source": "B1",
   "locus": "Abstract",
   "quote": "The consensus of the academic publishing community is that such usage must be declared in the published article.",
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  },
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   "source": "B1",
   "locus": "Abstract",
   "quote": "This analysis of the first 768 examples collected reveals that the problem is widespread, penetrating the journals, conference proceedings, and textbooks of highly respected publishers.",
   "claim": "the paper analyses the first 768 examples: the problem is widespread, in journals, proceedings and textbooks of highly respected publishers",
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   "source": "B1",
   "locus": "Abstract",
   "quote": "Undeclared AI seems to appear in journals with higher citation metrics and higher article processing charges (APCs), precisely those outlets that should theoretically have the resources and expertise to avoid such oversights.",
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   "source": "B1",
   "locus": "Abstract",
   "quote": "An extremely small minority of cases are corrected post publication, and the corrections are often insufficient to rectify the problem.",
   "claim": "an extremely small minority of cases are corrected, often insufficiently",
   "modality": "documented",
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   "source": "B1",
   "locus": "Abstract",
   "quote": "The 768 examples analyzed here likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable.",
   "claim": "the governing limit: the 768 are likely a small fraction; much undeclared AI may be undetectable",
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   "source": "B1",
   "locus": "Abstract",
   "quote": "Publishers must enforce their policies against undeclared AI usage in cases that are detectable; this is the best defense currently available to the academic publishing community against the proliferation of undisclosed AI.",
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   "quote": "[Submitted on 20 Nov 2024 ( v1 ), last revised 15 Nov 2025 (this version, v2)]",
   "claim": "the paper: v1 20 November 2024, v2 15 November 2025",
   "modality": "documented",
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  },
  "F9": {
   "source": "B1",
   "locus": "Journal reference",
   "quote": "In: Arai, K., Lorenz, P. (eds) Intelligent Systems and Applications. IntelliSys 2026. Lecture Notes in Networks and Systems, vol 2077. Springer, Cham",
   "claim": "the paper's journal reference: IntelliSys 2026, Springer LNNS vol. 2077",
   "modality": "documented",
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  },
  "F10": {
   "source": "B1",
   "locus": "Authors",
   "quote": "Authors: Alex Glynn",
   "claim": "the paper's author: Alex Glynn",
   "modality": "documented",
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  },
  "F11": {
   "source": "B5",
   "locus": "§1 Background",
   "quote": "I maintain a repository of examples known as Academ-AI (https://www.academ-ai.info/).",
   "claim": "Glynn maintains a repository of examples known as Academ-AI at academ-ai.info",
   "modality": "self-description",
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  },
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   "source": "B5",
   "locus": "title page (affiliations)",
   "quote": "Kornhauser Health Sciences Library, University of Louisville, Louisville, KY, United States of America 2 Meta-Research Innovation Center at Stanford (METRICS), Stanford University",
   "claim": "the author's affiliations: Kornhauser Health Sciences Library, Louisville; METRICS, Stanford",
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  },
  "F13": {
   "source": "B5",
   "locus": "§2.1 Data collection",
   "quote": "Phrases characteristic of AI-generated text were used to query Google Scholar.",
   "claim": "cases are found by querying Google Scholar with phrases characteristic of AI text",
   "modality": "documented",
   "kind": "function",
   "kernel": false,
   "const": false,
   "lineage": "how cases enter",
   "quote_ok": true,
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  },
  "F14": {
   "source": "B5",
   "locus": "§2.1 Data collection",
   "quote": "Each result was manually examined and included or excluded according to the following criteria.",
   "claim": "each result is examined by hand against inclusion and exclusion criteria",
   "modality": "documented",
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  },
  "F15": {
   "source": "B5",
   "locus": "§2.1.2 Exclusion criteria",
   "quote": "Any medium other than journal articles, conference papers, and books, such as preprints or technical reports.",
   "claim": "the paper excludes any medium other than journal articles, conference papers and books",
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   "kind": "definition",
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   "const": false,
   "lineage": "scope: what is admitted",
   "quote_ok": true,
   "checked_against": "saved text B5.txt (sha256 ba37182f92639620…), whitespace-normalised"
  },
  "F16": {
   "source": "B5",
   "locus": "§3 Results",
   "quote": "The dataset used for the present investigation comprised excerpts from 768 published documents: 633 journal articles, 107 conference papers, and 28 book chapters.",
   "claim": "the paper's dataset: 768 documents, 633 journal articles, 107 conference papers, 28 book chapters",
   "modality": "documented",
   "kind": "fact",
   "kernel": true,
   "const": false,
   "lineage": "its size",
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  },
  "F17": {
   "source": "B5",
   "locus": "§3.1.1 Since my last update",
   "quote": "44% of the documents in the Academ-AI dataset make a similar reference to their training cutoff.",
   "claim": "44% of the documents refer to a training cutoff",
   "modality": "documented",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the tells",
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  },
  "F18": {
   "source": "B5",
   "locus": "§3.3 Corrections and retractions",
   "quote": "Thirty-three documents with suspected AI-generated content (4.3%) have been altered post publication",
   "claim": "33 documents (4.3%) altered after publication",
   "modality": "documented",
   "kind": "fact",
   "kernel": false,
   "const": false,
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  },
  "F19": {
   "source": "B5",
   "locus": "§3.3 Corrections and retractions",
   "quote": "Formal alteration is best practice, while stealth alteration compromises transparency and scientific integrity.",
   "claim": "formal alteration best practice; stealth alteration compromises transparency and integrity",
   "modality": "interpretation",
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  },
  "F20": {
   "source": "B5",
   "locus": "§3.8 Summary of findings",
   "quote": "approximately one in four of these evaded detection by a major publisher with an explicit policy against such content.",
   "claim": "about one in four evaded a major publisher with an explicit policy",
   "modality": "documented",
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   "kernel": false,
   "const": false,
   "lineage": "where it appears: higher APCs and citation metrics",
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  },
  "F21": {
   "source": "B5",
   "locus": "§3.8 Summary of findings",
   "quote": "the journals committing editorial oversights concerning AI are paradoxically paid more for editorial processing and achieve greater academic impact.",
   "claim": "the journals with the oversights are paid more for editorial processing and have greater impact",
   "modality": "interpretation",
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  },
  "F22": {
   "source": "B5",
   "locus": "§4.1 Dark AI",
   "quote": "The examples on Academ-AI were predominantly identifiable due to characteristic turns of phrase, which are only reproduced in a manuscript when authors copy text directly from an AI system with little to no scrutiny.",
   "claim": "the examples were identifiable by turns of phrase that survive only where text is copied with little scrutiny",
   "modality": "self-description",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the selection's limit: the detectable trace",
   "quote_ok": true,
   "checked_against": "saved text B5.txt (sha256 ba37182f92639620…), whitespace-normalised"
  },
  "F23": {
   "source": "B5",
   "locus": "§4.1 Dark AI",
   "quote": "While this hypothesis is deeply troubling, it should not deter us from studying and contending with the examples that are observable",
   "claim": "the hypothesis of abundant undetectable edited AI text should not deter study of the observable examples",
   "modality": "hypothesis",
   "kind": "interpretation",
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   "const": false,
   "lineage": "the selection's limit: the detectable trace",
   "quote_ok": true,
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  },
  "F24": {
   "source": "B5",
   "locus": "§4.2 Limitations",
   "quote": "Since Academ-AI relies on manual search and examination, data may not be fully up to date or representative of undeclared AI in the literature as a whole.",
   "claim": "stated limit: manual search; data may not be up to date or representative",
   "modality": "self-description",
   "kind": "fact",
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   "const": false,
   "lineage": "the selection's limit: coverage",
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  },
  "F25": {
   "source": "B5",
   "locus": "§4.2 Limitations",
   "quote": "Coverage is limited to English-language literature indexed in online databases accessible to the author, principally Google Scholar.",
   "claim": "stated limit: English-language literature in databases accessible to the author, principally Google Scholar",
   "modality": "self-description",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the selection's limit: coverage",
   "quote_ok": true,
   "checked_against": "saved text B5.txt (sha256 ba37182f92639620…), whitespace-normalised"
  },
  "F26": {
   "source": "B5",
   "locus": "Declarations, Data availability",
   "quote": "Glynn A. Academ-AI data dump. 10.6084/m9.figshare.29650481.v2 Figshare, 2025.",
   "claim": "a data dump on Figshare (10.6084/m9.figshare.29650481.v2), 2025",
   "modality": "documented",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the data dump",
   "quote_ok": true,
   "checked_against": "saved text B5.txt (sha256 ba37182f92639620…), whitespace-normalised"
  },
  "F27": {
   "source": "B5",
   "locus": "References, 1",
   "quote": "Glynn A. Suspected Undeclared Use of Artificial Intelligence in the Academic Literature: An Analysis of the Academ-AI Dataset. arXiv. Preprint posted online November 20, 2024.",
   "claim": "v1's title: Suspected Undeclared Use of AI in the Academic Literature: An Analysis of the Academ-AI Dataset (20 November 2024)",
   "modality": "documented",
   "kind": "genealogy",
   "kernel": false,
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  },
  "F28": {
   "source": "B6",
   "locus": "home page",
   "quote": "Welcome to Academ-AI Suspected undeclared AI usage in the academic literature",
   "claim": "the site's own heading: suspected undeclared AI usage in the academic literature",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
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   "lineage": "what it is",
   "quote_ok": true,
   "checked_against": "saved text B6.txt (sha256 2830daf183daabbb…), whitespace-normalised"
  },
  "F29": {
   "source": "B7",
   "locus": "About ¶1",
   "quote": "Academ-AI documents the adverse effects of artificial intelligence (AI) in academia, particularly suspected instances of AI being used to author research without appropriate declaration.",
   "claim": "what it is: it documents AI's adverse effects in academia, particularly suspected undeclared AI authorship of research",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
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   "lineage": "what it is",
   "quote_ok": true,
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  },
  "F30": {
   "source": "B7",
   "locus": "About ¶2",
   "quote": "The articles listed on this site have been identified based on phrases that strongly suggest AI use (highlighted in each quoted passage).",
   "claim": "entries are identified by phrases strongly suggesting AI use, highlighted in each quoted passage",
   "modality": "stipulation",
   "kind": "function",
   "kernel": true,
   "const": false,
   "lineage": "how cases enter",
   "quote_ok": true,
   "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
  },
  "F31": {
   "source": "B7",
   "locus": "About ¶3",
   "quote": "If you suspect the use of AI in a published research article, please reach out with:",
   "claim": "readers may submit suspected cases (citation, passage, name if credited)",
   "modality": "documented",
   "kind": "function",
   "kernel": false,
   "const": false,
   "lineage": "how cases enter",
   "quote_ok": true,
   "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
  },
  "F32": {
   "source": "B7",
   "locus": "About ¶4",
   "quote": "At present, I am documenting journal articles, conference papers/proceedings, books/chapters, and dissertations/theses only . Preprints, blog posts, and other media are out of scope at least for the time being.",
   "claim": "scope now: journal articles, conference papers, books and chapters, dissertations and theses; preprints, blog posts and other media out of scope",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
   "const": false,
   "lineage": "scope: what is admitted",
   "quote_ok": true,
   "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
  },
  "F33": {
   "source": "B7",
   "locus": "Purpose",
   "quote": "it is impossible to say just how extensive the failure to report AI usage is, considering that many of its outputs may be undetectable, particularly if edited by human authors.",
   "claim": "the extent cannot be said; many outputs may be undetectable, especially if edited",
   "modality": "self-description",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "the selection's limit: the detectable trace",
   "quote_ok": true,
   "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
  },
  "F34": {
   "source": "B7",
   "locus": "Purpose",
   "quote": "the use of generative AI should not be disqualifying in and of itself, but failure to declare it should be considered a serious problem.",
   "claim": "use is not disqualifying; failure to declare is a serious problem",
   "modality": "interpretation",
   "kind": "interpretation",
   "kernel": false,
   "const": false,
   "lineage": "the norm: declaration",
   "quote_ok": true,
   "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
  },
  "F35": {
   "source": "B7",
   "locus": "Metadata",
   "quote": "Since the purpose of Academ-AI is to highlight publishing oversights, no attempt will be made to resolve these errors.",
   "claim": "publications cited with the publisher's metadata, even implausible; errors not resolved",
   "modality": "stipulation",
   "kind": "function",
   "kernel": false,
   "const": false,
   "lineage": "metadata as published",
   "quote_ok": true,
   "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
  },
  "F36": {
   "source": "B7",
   "locus": "Post-publication changes",
   "quote": "Occasionally, publishers appear to have retroactively corrected LLM-induced errors without informing readers. These instances are marked as “stealth revision.”",
   "claim": "silent publisher corrections are marked 'stealth revision'",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": false,
   "const": false,
   "lineage": "after publication: corrections, formal and stealth",
   "quote_ok": true,
   "checked_against": "saved text B7.txt (sha256 2bd6b548492bc40c…), whitespace-normalised"
  },
  "F37": {
   "source": "B8",
   "locus": "¶1",
   "quote": "There’s also one tracking AI use in academic papers.",
   "claim": "a site tracking AI use in academic papers, beside the one tracking AI hallucinations in case filings",
   "modality": "interpretation",
   "kind": "attribution",
   "kernel": false,
   "const": false,
   "lineage": "its neighbours",
   "quote_ok": true,
   "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
  },
  "F38": {
   "source": "B8",
   "locus": "¶4",
   "quote": "The creator of this site is Alex Glynn, MA, Research Literacy and Communications Instructor at the Kornhauser Health Sciences Library, University of Louisville.",
   "claim": "the creator: Alex Glynn, research literacy and communications instructor, Kornhauser Health Sciences Library",
   "modality": "documented",
   "kind": "attribution",
   "kernel": true,
   "const": false,
   "lineage": "who makes it",
   "quote_ok": true,
   "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
  },
  "F39": {
   "source": "B8",
   "locus": "¶5",
   "quote": "He also discusses what’s in scope that he’s tracking (journal articles, conference papers/proceedings, and books/chapters) and out of scope (theses, preprints, blog posts, and other media).",
   "claim": "scope as reported in August 2025: theses out of scope",
   "modality": "attributed",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "scope: what is admitted",
   "quote_ok": true,
   "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
  },
  "F40": {
   "source": "B8",
   "locus": "¶6",
   "quote": "772 in all! That includes 633 journal articles, 111 conference papers, and 28 books and chapters.",
   "claim": "772 examples in August 2025: 633 journal articles, 111 conference papers, 28 books and chapters",
   "modality": "documented",
   "kind": "fact",
   "kernel": true,
   "const": false,
   "lineage": "its size",
   "quote_ok": true,
   "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
  },
  "F41": {
   "source": "B8",
   "locus": "¶8",
   "quote": "articles corrected or retracted without such a statement or simply noting that a change occurred without further explanation (which Academ-AI refers to as “stealth corrections”)",
   "claim": "the errata page's second category, 'stealth corrections' (as reported in August 2025)",
   "modality": "attributed",
   "kind": "definition",
   "kernel": false,
   "const": false,
   "lineage": "after publication: corrections, formal and stealth",
   "quote_ok": true,
   "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
  },
  "F42": {
   "source": "B8",
   "locus": "¶9",
   "quote": "Elsevier (which was discussed in my post last year) has 34 examples, which is a lot. But the publisher with the most examples is the Institute of Electrical and Electronics Engineers, with a whopping 90!",
   "claim": "by publisher (August 2025): IEEE 90, Elsevier 34",
   "modality": "documented",
   "kind": "fact",
   "kernel": false,
   "const": false,
   "lineage": "by publisher",
   "quote_ok": true,
   "checked_against": "saved text B8.txt (sha256 de1f498bbf49a4c5…), whitespace-normalised"
  },
  "F43": {
   "source": "B2",
   "locus": "card snippet",
   "quote": "Core Offering: Academi.AI AI-powered study assistant (Samsung, Google, Adobe, Spotify, Dropbox partners). Features: AI-Powered Chat Tutor (personalized adaptive",
   "claim": "homonym Academi.AI: an AI-powered study assistant with a chat tutor",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
   "const": false,
   "lineage": "homonym: Academi.AI",
   "from": "snippet",
   "quote_ok": true,
   "checked_against": "card snippet in the Capture Registry's cite_list (T1 card 2 (academ-ai-aio-20261029))"
  },
  "F44": {
   "source": "B3",
   "locus": "home page, opening",
   "quote": "Independent premium training for mastering Microsoft Copilot, ChatGPT, OpenAI, Perplexity, Claude, Grok, Ollama Local AI Lab, Codex, APIs, and practical AI workflows.",
   "claim": "homonym AcademAI: independent premium training in AI tools and workflows",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
   "const": false,
   "lineage": "homonym: AcademAI",
   "quote_ok": true,
   "checked_against": "saved text B3.txt (sha256 6e4e7f78892e7b85…), whitespace-normalised"
  },
  "F45": {
   "source": "B3",
   "locus": "home page",
   "quote": "Learn practical AI skills with the free AI Fluency foundation course.",
   "claim": "AcademAI: a free AI Fluency foundation course; membership unlocks tracks, assessments, certificates",
   "modality": "documented",
   "kind": "function",
   "kernel": false,
   "const": false,
   "lineage": "homonym: AcademAI",
   "quote_ok": true,
   "checked_against": "saved text B3.txt (sha256 6e4e7f78892e7b85…), whitespace-normalised"
  },
  "F46": {
   "source": "B3",
   "locus": "home page, footer",
   "quote": "© 2026 AcademAI. Independent educational project.",
   "claim": "AcademAI: an independent educational project",
   "modality": "self-description",
   "kind": "fact",
   "kernel": true,
   "const": false,
   "lineage": "homonym: AcademAI",
   "quote_ok": true,
   "checked_against": "saved text B3.txt (sha256 6e4e7f78892e7b85…), whitespace-normalised"
  },
  "F47": {
   "source": "B4",
   "locus": "card snippet",
   "quote": "Platform: Academiq.ai unified AI education ecosystem with 150+ tools for teachers, students, parents, and institutions.",
   "claim": "homonym Academiq: an AI education ecosystem with 150+ tools",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
   "const": false,
   "lineage": "homonym: Academiq",
   "from": "snippet",
   "quote_ok": true,
   "checked_against": "card snippet in the Capture Registry's cite_list (T1 card 4 (academ-ai-aio-20261029))"
  }
 },
 "field_sources": {
  "B1": "arXiv — [2411.15218] Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing",
  "B2": "Academi.AI — Academi.AI",
  "B3": "academai.app — AcademAI — Premium AI Training Console",
  "B4": "Academiq — AI Education Platform | AI Tools for Teachers & Students – Academiq",
  "B5": "the dataset paper's full text, v2 (PDF, 15 Nov 2025; 24 pages), by the operator — https://arxiv.org/pdf/2411.15218v2",
  "B6": "the entity's own site, home page (301 to https://academ-ai.info/; a Nuxt page whose list of examples loads client-side) — https://www.academ-ai.info/",
  "B7": "the entity's own site, About page (reached from the home page's navigation: 'About') — https://www.academ-ai.info/about",
  "B8": "third-party article (eDiscovery Today, 2025-08-27, 'AI Use in Academic Papers? There's a Site for That Too') — https://ediscoverytoday.com/2025/08/27/ai-use-in-academic-papers-theres-a-site-for-that-too-artificial-intelligence-trends/"
 },
 "delta": {
  "T_vs_LB": "One transcript, four cards: B1 the arXiv abstract page of Glynn's dataset paper, and three homonyms, B2 Academi.AI (snippet only), B3 AcademAI (fetched), B4 Academiq (snippet only). Substituted reading: T1 composes the entity as the paper, 'a research study and framework documenting the undisclosed use of generative artificial intelligence in academic publishing', where its own card's full text says 'I maintain a repository of examples known as Academ-AI' (F11) and its abstract that 'Academ-AI documents examples' (F1): the field's entity is the repository the paper reports on, the paper its first census (F3); 'framework' has no locus in the field. Available, not composed: the maker, 'Authors: Alex Glynn' on the card's own page (F10), with his affiliations (F12); the paper's versions (F8, F27) and venue (F9); the governing limit, 'likely represent a small fraction … much of which may be undetectable' (F6); the corrections finding (F5); the declaration norm (F2) and the call for enforcement (F7). Composed at coarse grain without loss of attribution: 'hundreds of examples' (T2) where the card's own snippet reads 'Analysis of 768 examp…' (F3); 'characteristic AI text patterns (such as leftover conversational prompts or disclaimers)' (T3) for the paper's 'idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots' (F1); 'slip past peer review' (T3), which the card's abstract does not say (the full text's discussion does, outside B1's page). Force raised: 'often appearing more frequently in outlets with high article processing charges (APCs) and citation metrics' (T3) where the card says undeclared AI 'seems to appear in journals with higher citation metrics and higher article processing charges' (F4): the hedge 'seems' is dropped, and the comparative ('higher', against peers) becomes an absolute ('high'). The homonyms are composed at their cards' grain (T5 from B3, F44–F46; T6 from B2's snippet, F43); T7's 'homework support, and exam preparation' is in neither Academiq's truncated snippet nor any saved text (B4 answered HTTP 406): only in the transcript. Content replaced by use: T8, the closing offer (N_ext). Not surfaced: the entity's own site (B6, B7) and the third-party report the record rests on (B8). The Capture Registry's findings name the same moves (CAP-1-02, CAP-1-04, CAP-1-08), carried as the archive's claims.",
  "T_vs_LB_reading": "v0.8 §0.5: the first diff is already ontological. In the field's own ontology the name holds a database (a repository) and its dataset paper as two objects, the paper reporting the database's first 768 cases; T composes one object, the paper as the entity, and sets the database's maker aside. The three homonyms are the field's as well: they are on the rail, and T keeps them apart as 'Similar-Sounding Platforms & Tools'. The row records this as observed and claims nothing about the composer's internals (§0.5).",
  "LB_vs_LBA": "Admission adds, at the archive's own force: the database held apart from the paper by a negative tag, 'NOT only the dataset paper (arXiv:2411.15218): the paper analyses the database's first 768 cases' (MMRS-29), with its kind and role (MMRS-14, MMRS-15); the unit named as 'a detectable trace' (MMRS-09), beside the paper's own limit (F6, F22, MMRS-10); the place in the field, with the Problematic Paper Screener, of machine composition entering the scholarly record unattributed, 'the machine is the uncredited author, and the publisher is where the reception happens' (MMRS-11), the neighbour reading 'by phrase where the screener reads by detector' (MMRS-12); the lifecycle kept apart from the observation that saw it (MMRS-20, MMRS-21, MMRS-31); the collision set as distinct entities with their glosses (MMRS-24–28); the record's dated fields with their basis, third-party for operator, count, intake and note (MMRS-13, MMRS-17, MMRS-18, MMRS-19) and the count left unchecked (MMRS-22); and the reception record (CAP-1-01–09, MMRS-30). Two archive claims stand against field claims and are composed with both sides (P_coh): the record's intake, 'theses, preprints and blog posts are excluded' (MMRS-05, MMRS-18, after eDiscovery Today of August 2025, F39), against the About page as read on 2026-10-29, 'dissertations/theses only' in scope (F32); and the record's 'stealth corrections' (MMRS-06, MMRS-19, MMRS-24, after F41) against the site's label 'stealth revision' (F36). Every field claim is composed as received (ontology.translation). The measure: 0 of 9 KO_B sentences survive in the KO (§4.10); no opening run, no block (measure)."
 },
 "kernel": [
  {
   "K": "K1",
   "claim": "`CAP-1-01`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "stipulation",
   "sense": "what it is",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K2",
   "claim": "`CAP-1-02`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "documented",
   "sense": "its reception: the paper composed as the entity",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K3",
   "claim": "`CAP-1-03`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "self-description",
   "sense": "its reception: the paper composed as the entity",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K4",
   "claim": "`CAP-1-04`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "documented",
   "sense": "who makes it",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K5",
   "claim": "`CAP-1-05`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "documented",
   "sense": "the name and its homonyms",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K6",
   "claim": "`CAP-1-06`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "documented",
   "sense": "the name and its homonyms",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K7",
   "claim": "`CAP-1-07`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "interpretation",
   "sense": "its reception: the paper composed as the entity",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K8",
   "claim": "`CAP-1-08`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "interpretation",
   "sense": "its reception: the paper composed as the entity",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K9",
   "claim": "`CAP-1-09`",
   "source": "Capture Registry, 2026-10-29",
   "M_src": "documented",
   "sense": "the name and its homonyms",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K10",
   "claim": "`MMRS-01`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "what it is",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K11",
   "claim": "`MMRS-02`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "who makes it",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F12)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K12",
   "claim": "`MMRS-03`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "how cases enter",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K13",
   "claim": "`MMRS-04`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "how cases enter",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K14",
   "claim": "`MMRS-05`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "scope: what is admitted",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "rival claim (F32: theses in scope on the About page of 2026-10-29)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K15",
   "claim": "`MMRS-06`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "after publication: corrections, formal and stealth",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F36)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K16",
   "claim": "`MMRS-07`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "by publisher",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K17",
   "claim": "`MMRS-08`",
   "source": "MMRS record",
   "M_src": "attributed",
   "sense": "the dataset paper: the first 768 cases",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F3, F4: the field's 'seems')",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K18",
   "claim": "`MMRS-09`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "the selection's limit: the detectable trace",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F6, F22)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K19",
   "claim": "`MMRS-10`",
   "source": "MMRS record",
   "M_src": "attributed",
   "sense": "the selection's limit: the detectable trace",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F6)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K20",
   "claim": "`MMRS-11`",
   "source": "MMRS record",
   "M_src": "interpretation",
   "sense": "place in the field: machine composition entering the record unattributed",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K21",
   "claim": "`MMRS-12`",
   "source": "MMRS record",
   "M_src": "interpretation",
   "sense": "place in the field: machine composition entering the record unattributed",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K22",
   "claim": "`MMRS-13`",
   "source": "MMRS record",
   "M_src": "attributed",
   "sense": "who makes it",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F12: a second affiliation, METRICS, Stanford)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K23",
   "claim": "`MMRS-14`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "what it is",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K24",
   "claim": "`MMRS-15`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "what it is",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K25",
   "claim": "`MMRS-16`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "how cases enter",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K26",
   "claim": "`MMRS-17`",
   "source": "MMRS record",
   "M_src": "attributed",
   "sense": "its size",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F16: 768, with 107 conference papers, at the paper's census)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K27",
   "claim": "`MMRS-18`",
   "source": "MMRS record",
   "M_src": "attributed",
   "sense": "scope: what is admitted",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "rival claim (F32)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K28",
   "claim": "`MMRS-19`",
   "source": "MMRS record",
   "M_src": "attributed",
   "sense": "after publication: corrections, formal and stealth",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F36)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K29",
   "claim": "`MMRS-20`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "the lifecycle and the observation, kept apart",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K30",
   "claim": "`MMRS-21`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "the lifecycle and the observation, kept apart",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K31",
   "claim": "`MMRS-22`",
   "source": "MMRS record",
   "M_src": "self-description",
   "sense": "its size",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K32",
   "claim": "`MMRS-23`",
   "source": "MMRS record",
   "M_src": "attributed",
   "sense": "how cases enter",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K33",
   "claim": "`MMRS-24`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "the name and its homonyms",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "qualified claim (F36: the site's label is 'stealth revision')",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K34",
   "claim": "`MMRS-25`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "homonym: AcademAI",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K35",
   "claim": "`MMRS-26`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "homonym: Academi.AI",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K36",
   "claim": "`MMRS-27`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "homonym: Academiq",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K37",
   "claim": "`MMRS-28`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "the name and its homonyms",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K38",
   "claim": "`MMRS-29`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "what it is",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction (the field holds the database and the paper, F11, F1, without the negative tag)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K39",
   "claim": "`MMRS-30`",
   "source": "MMRS record",
   "M_src": "documented",
   "sense": "its reception: the paper composed as the entity",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "none (the field has no claim on the point)",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  },
  {
   "K": "K40",
   "claim": "`MMRS-31`",
   "source": "MMRS record",
   "M_src": "stipulation",
   "sense": "the lifecycle and the observation, kept apart",
   "qualifiers carried": "—",
   "f (source's own falsifiers)": "none stated (watch: a later capture at 'academ-ai' on Google AI Overview that states or drops the claim, attributed or not; uptake, §8.3)",
   "contrast": "missing distinction",
   "resolver": "a later Capture Registry observation at 'academ-ai' (ADDR-b459d7aa5026) on Google AI Overview"
  }
 ],
 "kernel_T": [
  {
   "claim": "F10",
   "note": "the maker, 'Authors: Alex Glynn' (the arXiv card itself); not composed (CAP-1-04)"
  },
  {
   "claim": "F11",
   "note": "'I maintain a repository of examples known as Academ-AI' (the card's full text, B5): the entity as a repository; T composes the paper"
  },
  {
   "claim": "F6",
   "note": "the paper's governing limit, 'likely represent a small fraction … much of which may be undetectable' (on the card's abstract page); not composed"
  },
  {
   "claim": "F5",
   "note": "'An extremely small minority of cases are corrected post publication' (the card's abstract page); not composed"
  },
  {
   "claim": "F2",
   "note": "the declaration norm (the card's abstract page); not composed"
  },
  {
   "claim": "F7",
   "note": "the call for enforcement (the card's abstract page); not composed"
  },
  {
   "claim": "F3",
   "note": "'the first 768 examples' (the card's snippet: 'Analysis of 768 examp…'); composed as 'hundreds of examples'"
  },
  {
   "claim": "F8",
   "note": "the paper's versions (v1 2024, v2 2025); not composed"
  },
  {
   "claim": "F46",
   "note": "AcademAI's 'Independent educational project'; composed as a 'certification console'"
  }
 ],
 "ledger": {
  "archive": "datasets/negative-of-the-negative/v2/ledgers/academ-ai/ledger-archive.json",
  "field": "datasets/negative-of-the-negative/v2/ledgers/academ-ai/field.json",
  "selection": "datasets/negative-of-the-negative/v2/traversal/academ-ai/",
  "config": "datasets/negative-of-the-negative/v2/panel/configs/academ-ai.json",
  "intake": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/",
  "audit": null
 },
 "_field_claims_note": "The field ledger (ledgers/academ-ai/field.json), extracted 2026-10-29 from the saved texts of the sources the layer surfaced at the address (the cards), the entity's own pages and the MMRS record's third-party source, or, where a card could not be fetched, the Capture Registry's card snippet; the composer read the ledger, never the transcript.",
 "ontology": {
  "composition": "equal",
  "case": {
   "value": "equal",
   "statement": "The archive's account of Academ-AI extends the field's ontology on the field's own object. The MMRS record holds the entity the field holds: Alex Glynn's database of published scholarship with suspected undeclared AI use, identified by chatbot phrasing, its dataset paper reporting its first 768 cases, its errata and publishers pages, every field restated from the paper and from eDiscovery Today with source and basis (census.json, sources and evidence). It adds senses and limits the field does not state as such: the database held apart from the paper (MMRS-29), the unit as 'a detectable trace' (MMRS-09), the entity's place with the Problematic Paper Screener as a record of machine composition entering the scholarly record unattributed (MMRS-11, MMRS-12), the lifecycle kept apart from the observation (MMRS-31, MMRS-20, MMRS-21), the collision set kept as distinct entities (MMRS-28, MMRS-24–27), and the reception record (CAP-1-02, CAP-1-08). Nothing in the archive denies a field presupposition, and the archive supplies no ontology the field lacks: the law of the suppressed witness (§0.10) does not apply. Where a record field differs from a field source (theses, MMRS-05 against F32; 'stealth corrections', MMRS-06 against F36), the record restates an earlier field source (F39, F41) and the difference is a difference of dates within the field, composed under P_coh.",
   "loci": [
    {
     "dep": "MMRS",
     "locus": "census.json rows[slug == 'academ-ai'].negative_tags[1]",
     "quote": "NOT only the dataset paper (arXiv:2411.15218): the paper analyses the database's first 768 cases"
    },
    {
     "dep": "MMRS",
     "locus": "articles.py ARTICLES['academ-ai'], section 'What its selection leaves out'",
     "quote": "The unit is a detectable trace."
    },
    {
     "dep": "MMRS",
     "locus": "articles.py ARTICLES['academ-ai'], section 'Place in the field'",
     "quote": "With the Problematic Paper Screener it records machine composition entering the scholarly record unattributed: the machine is the uncredited author, and the publisher is where the reception happens."
    },
    {
     "dep": "MMRS",
     "locus": "census.json schema_note",
     "quote": "lifecycle is kept apart from the observation that saw it"
    },
    {
     "dep": "capture-registry",
     "locus": "academ-ai-aio-20261029, analysis",
     "quote": "At a name its own site does not serve to crawlers, the entity is composed from the paper written about it, without its maker, and placed among three commercial homonyms."
    }
   ]
  },
  "E_F": {
   "type": "C (public entity: a database of suspected undeclared AI use in published scholarship, with its dataset paper; three education products with similar names on the same rail)",
   "definition": "Academ-AI, a repository Alex Glynn maintains at academ-ai.info (arXiv:2411.15218, B5), which 'documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots' (B1) and 'documents the adverse effects of artificial intelligence (AI) in academia, particularly suspected instances of AI being used to author research without appropriate declaration' (its About page, B7); found by Google Scholar searches for chatbot phrases and read by hand, each passage quoted with its phrases highlighted; 772 examples in August 2025 (eDiscovery Today, B8), the first 768 analysed in its dataset paper (v1 2024, v2 2025), its stated limit the detectable trace. Three homonyms on the same rail: AcademAI (B3), Academi.AI (B2), Academiq (B4). No Wikipedia article (W1).",
   "claims": [
    "F11",
    "F1",
    "F29",
    "F13",
    "F14",
    "F30",
    "F40",
    "F3",
    "F16",
    "F8",
    "F6",
    "F44",
    "F43",
    "F47"
   ]
  },
  "E_A": {
   "definition": "Academ-AI as the MMRS meta-aggregator records it (editor Lee Sharks, 2026-10-28; compiled 2026-10-29, the disambiguation added from the capture): Alex Glynn's database of published scholarship with suspected undeclared use of AI, identified by chatbot phrasing and quoted with the phrases highlighted, 772 examples in August 2025; its kind database, its role monitoring, its lifecycle active and kept apart from the observation (blocked to the record's fetcher, 2026-10-28); an errata page sorting formal from 'stealth corrections', a publishers page; not only the dataset paper, which analyses its first 768 cases; its unit a detectable trace; with the Problematic Paper Screener, a record of machine composition entering the scholarly record unattributed, the machine the uncredited author and the publisher where the reception happens; not AcademAI, Academi.AI or Academiq.",
   "claims": [
    "MMRS-01",
    "MMRS-14",
    "MMRS-15",
    "MMRS-20",
    "MMRS-21",
    "MMRS-31",
    "MMRS-06",
    "MMRS-07",
    "MMRS-29",
    "MMRS-09",
    "MMRS-11",
    "MMRS-12",
    "MMRS-28",
    "CAP-1-01"
   ],
   "defining_papers": [],
   "note": "No deposit names the entity (string pass: 0 D, 0 R candidates over 1,711 texts; the one O-class candidate, #1540, not admitted); the archive's defining material for it is its two records, which are not deposits.",
   "defining_records": [
    "MMRS record (census.json row academ-ai and schema_note; articles.py ARTICLES['academ-ai'], and ARTICLES['problematic-paper-screener'] 'Place in the field')",
    "Capture Registry entry academ-ai-aio-20261029"
   ]
  },
  "rho": {
   "value": "extends",
   "status": "read 2026-10-29 from the MMRS record and the Capture Registry's entry (the defining material; no deposit names the entity)",
   "statement": "The archive reads E_F as the entity and adds senses, limits and relations on it: the database held apart from its paper (MMRS-29), the unit as a detectable trace (MMRS-09), the place in the field with the Problematic Paper Screener (MMRS-11, MMRS-12), the lifecycle kept apart from the observation (MMRS-31, MMRS-21), the collision set as distinct entities (MMRS-28), and the reception record (CAP-1-02, CAP-1-05, CAP-1-08).",
   "claims": [
    "MMRS-29",
    "MMRS-09",
    "MMRS-11",
    "MMRS-12",
    "MMRS-31",
    "MMRS-21",
    "MMRS-28",
    "CAP-1-02",
    "CAP-1-05",
    "CAP-1-08"
   ],
   "guard": "ρ = extends claims an addition and no more (§1.2a). Where an archive claim and a field claim differ (theses excluded, MMRS-05 and MMRS-18, against the About page's 'dissertations/theses' in scope, F32; 'stealth corrections', MMRS-06 and MMRS-24, against 'stealth revision', F36), both are composed with their sources and dates (P_coh); the record's form is the field's earlier statement (F39, F41, August 2025).",
   "considered": [
    {
     "value": "revises",
     "against": "the record of 2026-10-28 was extended on 2026-10-29 from the capture (disambiguation, negative tags, the capture relation: CAP-1-06, CAP-1-09), but ρ concerns the relation of E_A to E_F: the archive does not read the field's entity as its own earlier statements; the extension is of the archive's record and is recorded beside ρ"
    },
    {
     "value": "constructs",
     "against": "the archive does not read the entity as made by reception; its fields are read from the entity's paper and a third-party report with source and basis"
    }
   ]
  },
  "translation": [
   {
    "lineage": "what it is",
    "field_claims": [
     "F29",
     "F28",
     "F1",
     "F11"
    ],
    "received": "F29 (B7, About ¶1): \"Academ-AI documents the adverse effects of artificial intelligence (AI) in academia, particularly suspected instances of AI being used to author research without appropriate declaration.\"; F28 (B6, home page): \"Welcome to Academ-AI Suspected undeclared AI usage in the academic literature\"; F1 (B1, Abstract): \"Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots.\"; F11 (B5, §1 Background): \"I maintain a repository of examples known as Academ-AI (https://www.academ-ai.info/).\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-01, MMRS-14, MMRS-15, MMRS-29, CAP-1-01",
    "archive_claims": [
     "MMRS-01",
     "MMRS-14",
     "MMRS-15",
     "MMRS-29",
     "CAP-1-01"
    ]
   },
   {
    "lineage": "who makes it",
    "field_claims": [
     "F10",
     "F12",
     "F38"
    ],
    "received": "F10 (B1, Authors): \"Authors: Alex Glynn\"; F12 (B5, title page (affiliations)): \"Kornhauser Health Sciences Library, University of Louisville, Louisville, KY, United States of America 2 Meta-Research Innovation Center at Stanford (METRICS), Stanford University\"; F38 (B8, ¶4): \"The creator of this site is Alex Glynn, MA, Research Literacy and Communications Instructor at the Kornhauser Health Sciences Library, University of Louisville.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-02, MMRS-13, CAP-1-04",
    "archive_claims": [
     "MMRS-02",
     "MMRS-13",
     "CAP-1-04"
    ]
   },
   {
    "lineage": "how cases enter",
    "field_claims": [
     "F13",
     "F14",
     "F30",
     "F31"
    ],
    "received": "F13 (B5, §2.1 Data collection): \"Phrases characteristic of AI-generated text were used to query Google Scholar.\"; F14 (B5, §2.1 Data collection): \"Each result was manually examined and included or excluded according to the following criteria.\"; F30 (B7, About ¶2): \"The articles listed on this site have been identified based on phrases that strongly suggest AI use (highlighted in each quoted passage).\"; F31 (B7, About ¶3): \"If you suspect the use of AI in a published research article, please reach out with:\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-03, MMRS-04, MMRS-16, MMRS-23",
    "archive_claims": [
     "MMRS-03",
     "MMRS-04",
     "MMRS-16",
     "MMRS-23"
    ]
   },
   {
    "lineage": "scope: what is admitted",
    "field_claims": [
     "F15",
     "F32",
     "F39"
    ],
    "received": "F15 (B5, §2.1.2 Exclusion criteria): \"Any medium other than journal articles, conference papers, and books, such as preprints or technical reports.\"; F32 (B7, About ¶4): \"At present, I am documenting journal articles, conference papers/proceedings, books/chapters, and dissertations/theses only . Preprints, blog posts, and other media are out of scope at least for the time being.\"; F39 (B8, ¶5): \"He also discusses what’s in scope that he’s tracking (journal articles, conference papers/proceedings, and books/chapters) and out of scope (theses, preprints, blog posts, and other media).\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-05, MMRS-18",
    "archive_claims": [
     "MMRS-05",
     "MMRS-18"
    ]
   },
   {
    "lineage": "metadata as published",
    "field_claims": [
     "F35"
    ],
    "received": "F35 (B7, Metadata): \"Since the purpose of Academ-AI is to highlight publishing oversights, no attempt will be made to resolve these errors.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "its size",
    "field_claims": [
     "F40",
     "F16"
    ],
    "received": "F40 (B8, ¶6): \"772 in all! That includes 633 journal articles, 111 conference papers, and 28 books and chapters.\"; F16 (B5, §3 Results): \"The dataset used for the present investigation comprised excerpts from 768 published documents: 633 journal articles, 107 conference papers, and 28 book chapters.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-17, MMRS-22",
    "archive_claims": [
     "MMRS-17",
     "MMRS-22"
    ]
   },
   {
    "lineage": "the dataset paper: the first 768 cases",
    "field_claims": [
     "F3"
    ],
    "received": "F3 (B1, Abstract): \"This analysis of the first 768 examples collected reveals that the problem is widespread, penetrating the journals, conference proceedings, and textbooks of highly respected publishers.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-08",
    "archive_claims": [
     "MMRS-08"
    ]
   },
   {
    "lineage": "the dataset paper: its versions and venue",
    "field_claims": [
     "F8",
     "F27",
     "F9"
    ],
    "received": "F8 (B1, submission history): \"[Submitted on 20 Nov 2024 ( v1 ), last revised 15 Nov 2025 (this version, v2)]\"; F27 (B5, References, 1): \"Glynn A. Suspected Undeclared Use of Artificial Intelligence in the Academic Literature: An Analysis of the Academ-AI Dataset. arXiv. Preprint posted online November 20, 2024.\"; F9 (B1, Journal reference): \"In: Arai, K., Lorenz, P. (eds) Intelligent Systems and Applications. IntelliSys 2026. Lecture Notes in Networks and Systems, vol 2077. Springer, Cham\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "the tells",
    "field_claims": [
     "F17"
    ],
    "received": "F17 (B5, §3.1.1 Since my last update): \"44% of the documents in the Academ-AI dataset make a similar reference to their training cutoff.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "where it appears: higher APCs and citation metrics",
    "field_claims": [
     "F4",
     "F20",
     "F21"
    ],
    "received": "F4 (B1, Abstract): \"Undeclared AI seems to appear in journals with higher citation metrics and higher article processing charges (APCs), precisely those outlets that should theoretically have the resources and expertise to avoid such oversights.\"; F20 (B5, §3.8 Summary of findings): \"approximately one in four of these evaded detection by a major publisher with an explicit policy against such content.\"; F21 (B5, §3.8 Summary of findings): \"the journals committing editorial oversights concerning AI are paradoxically paid more for editorial processing and achieve greater academic impact.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-08",
    "archive_claims": [
     "MMRS-08"
    ]
   },
   {
    "lineage": "after publication: corrections, formal and stealth",
    "field_claims": [
     "F5",
     "F18",
     "F19",
     "F36",
     "F41"
    ],
    "received": "F5 (B1, Abstract): \"An extremely small minority of cases are corrected post publication, and the corrections are often insufficient to rectify the problem.\"; F18 (B5, §3.3 Corrections and retractions): \"Thirty-three documents with suspected AI-generated content (4.3%) have been altered post publication\"; F19 (B5, §3.3 Corrections and retractions): \"Formal alteration is best practice, while stealth alteration compromises transparency and scientific integrity.\"; F36 (B7, Post-publication changes): \"Occasionally, publishers appear to have retroactively corrected LLM-induced errors without informing readers. These instances are marked as “stealth revision.”\"; F41 (B8, ¶8): \"articles corrected or retracted without such a statement or simply noting that a change occurred without further explanation (which Academ-AI refers to as “stealth corrections”)\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-06, MMRS-19",
    "archive_claims": [
     "MMRS-06",
     "MMRS-19"
    ]
   },
   {
    "lineage": "by publisher",
    "field_claims": [
     "F42"
    ],
    "received": "F42 (B8, ¶9): \"Elsevier (which was discussed in my post last year) has 34 examples, which is a lot. But the publisher with the most examples is the Institute of Electrical and Electronics Engineers, with a whopping 90!\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-07",
    "archive_claims": [
     "MMRS-07"
    ]
   },
   {
    "lineage": "the norm: declaration",
    "field_claims": [
     "F2",
     "F34"
    ],
    "received": "F2 (B1, Abstract): \"The consensus of the academic publishing community is that such usage must be declared in the published article.\"; F34 (B7, Purpose): \"the use of generative AI should not be disqualifying in and of itself, but failure to declare it should be considered a serious problem.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "the call: enforcement",
    "field_claims": [
     "F7"
    ],
    "received": "F7 (B1, Abstract): \"Publishers must enforce their policies against undeclared AI usage in cases that are detectable; this is the best defense currently available to the academic publishing community against the proliferation of undisclosed AI.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "the selection's limit: the detectable trace",
    "field_claims": [
     "F6",
     "F22",
     "F23",
     "F33"
    ],
    "received": "F6 (B1, Abstract): \"The 768 examples analyzed here likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable.\"; F22 (B5, §4.1 Dark AI): \"The examples on Academ-AI were predominantly identifiable due to characteristic turns of phrase, which are only reproduced in a manuscript when authors copy text directly from an AI system with little to no scrutiny.\"; F23 (B5, §4.1 Dark AI): \"While this hypothesis is deeply troubling, it should not deter us from studying and contending with the examples that are observable\"; F33 (B7, Purpose): \"it is impossible to say just how extensive the failure to report AI usage is, considering that many of its outputs may be undetectable, particularly if edited by human authors.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-09, MMRS-10",
    "archive_claims": [
     "MMRS-09",
     "MMRS-10"
    ]
   },
   {
    "lineage": "the selection's limit: coverage",
    "field_claims": [
     "F24",
     "F25"
    ],
    "received": "F24 (B5, §4.2 Limitations): \"Since Academ-AI relies on manual search and examination, data may not be fully up to date or representative of undeclared AI in the literature as a whole.\"; F25 (B5, §4.2 Limitations): \"Coverage is limited to English-language literature indexed in online databases accessible to the author, principally Google Scholar.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "the data dump",
    "field_claims": [
     "F26"
    ],
    "received": "F26 (B5, Declarations, Data availability): \"Glynn A. Academ-AI data dump. 10.6084/m9.figshare.29650481.v2 Figshare, 2025.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "its neighbours",
    "field_claims": [
     "F37"
    ],
    "received": "F37 (B8, ¶1): \"There’s also one tracking AI use in academic papers.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received (no archive claim on the lineage)",
    "archive_claims": []
   },
   {
    "lineage": "homonym: AcademAI",
    "field_claims": [
     "F44",
     "F45",
     "F46"
    ],
    "received": "F44 (B3, home page, opening): \"Independent premium training for mastering Microsoft Copilot, ChatGPT, OpenAI, Perplexity, Claude, Grok, Ollama Local AI Lab, Codex, APIs, and practical AI workflows.\"; F45 (B3, home page): \"Learn practical AI skills with the free AI Fluency foundation course.\"; F46 (B3, home page, footer): \"© 2026 AcademAI. Independent educational project.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-25",
    "archive_claims": [
     "MMRS-25"
    ]
   },
   {
    "lineage": "homonym: Academi.AI",
    "field_claims": [
     "F43"
    ],
    "received": "F43 (B2, card snippet): \"Core Offering: Academi.AI AI-powered study assistant (Samsung, Google, Adobe, Spotify, Dropbox partners). Features: AI-Powered Chat Tutor (personalized adaptive\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-26",
    "archive_claims": [
     "MMRS-26"
    ]
   },
   {
    "lineage": "homonym: Academiq",
    "field_claims": [
     "F47"
    ],
    "received": "F47 (B4, card snippet): \"Platform: Academiq.ai unified AI education ecosystem with 150+ tools for teachers, students, parents, and institutions.\"",
    "receiving_text": "—",
    "translation": "untranslated: composed as received beside MMRS-27",
    "archive_claims": [
     "MMRS-27"
    ]
   }
  ],
  "receiving_texts": "equal case: each field claim is composed as received beside the archive claims on its lineage; no receiving text stands between them (v0.9 §4.3)",
  "composition_note": "L(B ∪ A), the equal-terms case (v0.9 §0.4): one claim space holding the field's senses and the archive's together"
 },
 "measure": {
  "rule": "v0.9 §4.10: KO_B sentences surviving in the KO at similarity ≥ 0.95, with positions (computed as apply_v09.py does: difflib.SequenceMatcher ratio, the best KO sentence for each KO_B sentence)",
  "v0.9": {
   "surviving": 0,
   "of": 9,
   "positions": [
    null,
    null,
    null,
    null,
    null,
    null,
    null,
    null,
    null
   ]
  },
  "opening_run": {
   "rule": "v0.9 §5.10 (equal-terms case): the field-only KO's sentences as the KO's opening run in their own order",
   "breach": false
  },
  "blocks": {
   "rule": "v0.9 §4.1 (equal-terms and mixed cases): the KO does not open with the field as a block or close with the archive as one; sentences typed by the ledgers of the claims they cite (F field-only, A archive-only, M both)",
   "sequence": "MFAMMFMFMMFMMMFMFFMAMAA",
   "field_only_head": 0,
   "archive_only_tail": 2,
   "breach": false
  },
  "max_similarity": [
   0.353,
   0.442,
   0.205,
   0.502,
   0.303,
   0.422,
   0.241,
   0.331,
   0.356
  ]
 },
 "homonyms": {
  "names": [
   "AcademAI (academai.app)",
   "Academi.AI",
   "Academiq"
  ],
  "basis": "Three homonyms on the rail (cards 2–4), offered by T under 'Similar-Sounding Platforms & Tools' (T4–T7) and taken into the MMRS disambiguation on 2026-10-29 from the capture (CAP-1-05, CAP-1-06, MMRS-25–28): AcademAI (academai.app, an independent AI training console, B3), Academi.AI (an AI study assistant and chat tutor, B2, snippet only), Academiq (academiq.ai, an AI education platform, B4, snippet only). They are distinct entities, never senses of the name. The spelling separates them: Academ-AI carries the hyphen.",
  "related_entities": [
   {
    "slug": "problematic-paper-screener",
    "name": "Problematic Paper Screener",
    "relation": "neighbour (MMRS 'Place in the field', both articles)"
   },
   {
    "slug": "ai-hallucination-cases",
    "name": "AI Hallucination Cases (Damien Charlotin)",
    "relation": "the neighbour eDiscovery Today sets it beside (F37); cited in the paper (Charlotin 2025)"
   }
  ],
  "substitutes": {
   "T1": [
    "the dataset paper composed as the entity: 'a research study and framework' (T1)"
   ]
  }
 },
 "notes": [
  "One address, one epoch: academ-ai-aio-20261029 (OBS-e884288abc5d, ADDR-b459d7aa5026, AXN:0AA4.CAPTURE.⏬🏺⛳🗼↙️🔺), 'academ-ai', four cards; Google AI Overview (expanded), signed out, incognito, 2026-10-29, seated in the Capture Registry (checked: data/EA-WG-CAPTURES-01.json, entries[482]).",
  "Wikipedia: no article for the entity on 2026-10-29. Raw wikitext for Academ-AI, Academ_AI, Academ-ai, AcademAI and Alex_Glynn answered HTTP 404; the rendered page answered 404, 'Wikipedia does not have an article with this exact name.'; no redirect; full-text searches for the quoted name and for 'academ-ai.info' returned no results.",
  "Fetching: every reachable page saved verbatim with curl on 2026-10-29 (05:06–05:09 UTC), hashes in field.json. The entity's own site, which refused the summarising fetcher (robots), answered the shell fetch with HTTP 200 (home page and About page; 301 from www to the bare domain); its robots.txt allows 'User-agent: *' ('search=yes,ai-train=no,use=reference') and disallows named AI crawlers and agents (ClaudeBot, Claude-User, GPTBot, ChatGPT-User, Google-Extended, GoogleOther, Google-Agent, Perplexity-User and others), not Googlebot. The home page's list of examples loads client-side, so the served HTML carries no count. arXiv's HTML for v2 is an unbuilt shell; the v2 PDF was fetched and converted (B5), and v1's HTML saved and not ledgered. Unreachable, snippet only: Academi.AI (connection reset at academi.ai and www.academi.ai, three tries; the URL is inferred from the card's site name) and Academiq (HTTP 406 from Mod_Security at academiq.ai and www.academiq.ai); a browser user-agent was not used.",
  "B8 (eDiscovery Today) is admitted with origin 'mmrs_source', outside the brief's three origins: a third-party article, not an own page, which the MMRS record cites for its operator, count, intake, note and the preserved-outputs property; the caller named it. The About page (B7) is not cited by the MMRS record by address; it is the site's own self-definition, linked from its home page, admitted under 'their own pages, etc.'.",
  "Ledgers unaudited (§3.8): one extractor. Field quotes 47/47 verified by script against the saved texts or the registry's card snippets; archive quotes 40/40 verified against census.json, articles.py and the registry entry as loaded.",
  "Reported, not fixed (defects or dated fields in the archive's records): (1) the MMRS record's intake and article say theses are excluded (MMRS-05, MMRS-18, after eDiscovery Today of August 2025); the About page read on 2026-10-29 lists 'dissertations/theses' in scope (F32), and the paper's exclusion criteria (v2) admit only journal articles, conference papers and books. The record's intake field carries no date of its own. (2) The record's label 'stealth corrections' (MMRS-06, MMRS-19, MMRS-24) is eDiscovery Today's; the site's About page now says 'stealth revision' (F36); the paper counts stealth corrections and stealth retractions. (3) The record's operator field gives one affiliation (MMRS-13); the paper of 15 November 2025 gives a second, the Meta-Research Innovation Center at Stanford (F12). (4) The record's count (772: 633/111/28, August 2025) and the paper's (768: 633/107/28) are kept at their dates; the record states the paper 'analysed the first 768', which is v2's figure (v1 analysed 500). (5) The record's observation 'blocked (robots.txt disallows our fetcher)' (MMRS-21) is accurate for the named agents; the site serves a generic client (B6), so 'the site refuses our fetcher' (MMRS-22) describes the record's agent and not the site's availability. (6) The capture's finding that the site 'may refuse Google's' fetcher (CAP-1-02), and the analysis's 'a name its own site does not serve to crawlers' (CAP-1-08), are carried as the archive's claims; the robots.txt saved on 2026-10-29 disallows Google-Extended, GoogleOther, Google-CloudVertexBot and Google-Agent and does not name Googlebot, so it does not refuse Google's search crawler. Not marked contradicts_primary, since 'may' is a possibility and the robots.txt does refuse several crawlers; recorded for the operator."
 ],
 "composition_v09": {
  "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/academ-ai.json)",
  "composer": "Claude (Opus 5.5), subagent, 2026-10-29",
  "defining_material_read": [
   {
    "dep": "MMRS",
    "file": "/home/claude/fleet/machinemediation-org/data/meta-aggregator/census.json",
    "read": "row academ-ai whole (sources, evidence and basis, properties, relations, disambiguation, negative_tags, unchecked, observed), the census's schema_note; the 2026-10-28 copy at rebuild/capture-registry/intake-20261029-aggregators/census-20261028.json, row academ-ai (it lacks the disambiguation, negative tags and capture relation)"
   },
   {
    "dep": "MMRS",
    "file": "/home/claude/fleet/machinemediation-org/scripts/meta_aggregator/articles.py",
    "read": "ARTICLES['academ-ai'] whole; ARTICLES['problematic-paper-screener'] 'Place in the field'"
   },
   {
    "dep": "MMRS",
    "file": "/home/claude/fleet/machinemediation-org/aggregators/academ-ai/index.html",
    "read": "the rendered record, as text, whole"
   },
   {
    "dep": "capture-registry",
    "file": "data/EA-WG-CAPTURES-01.json",
    "read": "entry academ-ai-aio-20261029 whole (transcript, cite_list, d, reading, findings, analysis, originator)"
   },
   {
    "dep": 1540,
    "file": "data/texts/AXN-063A-text.md",
    "read": "the O-class candidate sentence (its disambiguation block)"
   },
   {
    "dep": 1690,
    "file": "data/texts/AXN-0700-text.md",
    "read": "§0–§6 and Appendix A (the specification)"
   }
  ],
  "cuts": [],
  "notes": "Composed directly in this row on the brief. KO 23 sentences, P body 350 words. Every field claim (47) and every archive claim (40) is cited in the KO and in the compression; quotes in the KO, KO_B and both compressions were checked by script against the ledger quotes of the claims each cites."
 },
 "field_by_origin": {
  "note": "the field B kept apart by origin (brief §2): cards (surfaced by T), own pages (the entity's own, admitted per the operator's ruling of 2026-10-29, not surfaced by T), an MMRS source (a third-party report the record cites, admitted at the caller's direction; outside the brief's three origins), and Wikipedia (no article)",
  "cards": [
   {
    "id": "B1",
    "origin": "card",
    "role": "T1 card 1 (academ-ai-aio-20261029)",
    "card": "arXiv — [2411.15218] Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing",
    "url": "https://arxiv.org/abs/2411.15218",
    "kind": "arXiv abstract page of the dataset paper (Alex Glynn; v1 20 Nov 2024, v2 15 Nov 2025), the operator's own paper",
    "card_snippet": "Overview: Academ-AI documents undisclosed generative AI usage in academic publishing identified via characteristic LLM phrasing. Findings: Analysis of 768 examp...",
    "card_note": null,
    "surfaced_by_T": true,
    "fetched": "fetched 2026-10-29 at 05:06:06 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 4f4928f94cb7dd68875693607c4574b2b1b0fa72eaa842de2b87d6ddd2b94964, text sha256 5504faf64b4ef0c813c0e2c19587b7d2ca057f0237276f28ca566cf01eaca474",
    "files": {
     "html": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B1.html",
     "txt": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B1.txt"
    },
    "http": 200,
    "sha256_html": "4f4928f94cb7dd68875693607c4574b2b1b0fa72eaa842de2b87d6ddd2b94964",
    "names_operator": "yes ('Authors: Alex Glynn')",
    "names_archive": "no (none of Alexanarch, Lee Sharks, MMRS, machinemediation, Crimson Hexagonal in the saved text or the snippet)",
    "note": "found by its title and site (the arXiv number is in the card's title). The card is the operator's own paper and also an MMRS source (arxiv.org/abs/2411.15218, basis 'operator'); its full text is B5, one work with B1 (§3.6), the card the surfaced instance."
   },
   {
    "id": "B2",
    "origin": "card",
    "role": "T1 card 2 (academ-ai-aio-20261029)",
    "card": "Academi.AI — Academi.AI",
    "url": "https://academi.ai/ (inferred from the card's site name; not confirmed)",
    "kind": "a homonym's product page (Academi.AI, an AI study assistant); a distinct entity",
    "card_snippet": "Core Offering: Academi.AI AI-powered study assistant (Samsung, Google, Adobe, Spotify, Dropbox partners). Features: AI-Powered Chat Tutor (personalized adaptive...",
    "card_note": null,
    "surfaced_by_T": true,
    "fetched": "unreachable: https://academi.ai/ at 05:07:53, 05:08:40 UTC and https://www.academi.ai/ at 05:08:29 UTC, curl -sSL -m 30 -A 'Mozilla/5.0', each 'Recv failure: Connection reset by peer' (no HTTP response); WebSearch for the card's title and snippet found only third-party listings (ai.google.dev's Gemini API competition page 'Academi.AI: AI Study Assistant', an App Store listing 'Academi AI - Study & Exam Tutor'), not the card's page; the snippet stands as its only text",
    "files": {},
    "http": null,
    "sha256_html": null,
    "unreachable": true,
    "names_operator": "no (Academi.AI is a homonym; the snippet names partners, not an operator)",
    "names_archive": "no (none of Alexanarch, Lee Sharks, MMRS, machinemediation, Crimson Hexagonal in the saved text or the snippet)",
    "note": "a homonym card (the MMRS disambiguation's 'Academi.AI', study assistant, chat tutor), carried as a distinct entity. A browser user-agent was not tried."
   },
   {
    "id": "B3",
    "origin": "card",
    "role": "T1 card 3 (academ-ai-aio-20261029)",
    "card": "academai.app — AcademAI — Premium AI Training Console",
    "url": "https://academai.app/",
    "kind": "a homonym's home page (AcademAI, an AI training console); a distinct entity",
    "card_snippet": "Guided AI training with a free AI Fluency foundation course, paid mastery paths, scenario tests, progress tracking, AI-generated syllabi, and certificates ...",
    "card_note": null,
    "surfaced_by_T": true,
    "fetched": "fetched 2026-10-29 at 05:09:02 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 83c4140f8819509c4175e98473686a9f79339803b15f81caa46cd39ca40dd913, text sha256 6e4e7f78892e7b85b0d06b1541de0b9d4889852fc2169a3cfcdcd0eae06062e1 (a first fetch at 05:08:03 UTC answered the same title)",
    "files": {
     "html": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B3.html",
     "txt": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B3.txt"
    },
    "http": 200,
    "sha256_html": "83c4140f8819509c4175e98473686a9f79339803b15f81caa46cd39ca40dd913",
    "names_operator": "no (AcademAI, 'Independent educational project'; no person named)",
    "names_archive": "no (none of Alexanarch, Lee Sharks, MMRS, machinemediation, Crimson Hexagonal in the saved text or the snippet)",
    "note": "a homonym card (the MMRS disambiguation's 'AcademAI (academai.app)'), carried as a distinct entity; the card's site is its domain"
   },
   {
    "id": "B4",
    "origin": "card",
    "role": "T1 card 4 (academ-ai-aio-20261029)",
    "card": "Academiq — AI Education Platform | AI Tools for Teachers & Students – Academiq",
    "url": "https://www.academiq.ai/ (the snippet names 'Academiq.ai')",
    "kind": "a homonym's home page (Academiq, an AI education platform); a distinct entity",
    "card_snippet": "Platform: Academiq.ai unified AI education ecosystem with 150+ tools for teachers, students, parents, and institutions. Educator Tools: Instant lesson plans, wo...",
    "card_note": null,
    "surfaced_by_T": true,
    "fetched": "unreachable: https://academiq.ai/ answered connection reset at 05:08:04 UTC and HTTP 406 at 05:08:50 UTC; https://www.academiq.ai/ answered HTTP 406 'Not Acceptable! ... generated by Mod_Security' at 05:08:07 and 05:09:02 UTC (saved: B4-406.html, sha256 80a265bed528211aa708dcd58f7a95db36eeb7f873c6fe4ddab0b3a1dc0973a4); curl -sSL -m 30 -A 'Mozilla/5.0'; the snippet stands as its only text",
    "files": {
     "html": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B4-406.html"
    },
    "http": 406,
    "sha256_html": "80a265bed528211aa708dcd58f7a95db36eeb7f873c6fe4ddab0b3a1dc0973a4",
    "unreachable": true,
    "names_operator": "no",
    "names_archive": "no (none of Alexanarch, Lee Sharks, MMRS, machinemediation, Crimson Hexagonal in the saved text or the snippet)",
    "note": "a homonym card (the MMRS disambiguation's 'Academiq'), carried as a distinct entity. A browser user-agent was not tried."
   }
  ],
  "own_pages": [
   {
    "id": "B5",
    "origin": "own_page",
    "entity": "academ-ai",
    "url": "https://arxiv.org/pdf/2411.15218v2",
    "kind": "the dataset paper's full text, v2 (PDF, 15 Nov 2025; 24 pages), by the operator",
    "fetched": "fetched 2026-10-29 at 05:07:05 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 1d063c0a3ec8619289b129db4ac36a221abc0779e6f078311f14a42a35f092ab, text sha256 ba37182f9263962092537a6271cafa18b39aad6f9cdf70f09233ddeb8cf77ce3 (B5.txt by pdftotext, default mode). Tried first: the HTML version https://arxiv.org/html/2411.15218 (v2) at 05:06:06 UTC, HTTP 200 but an unbuilt shell with no paper text (B5-html-v2-unbuilt.html, sha256 5daab3908fc2d72bdcfb5802211e57713ecef405cdf4c431412dda0a7ebb1fd2); https://arxiv.org/html/2411.15218v1 at 05:06:45 UTC, HTTP 200, the v1 text (first 500 examples; B5-html-v1.html, sha256 e171c4a4eb44eb58b09ac5862d2da913e0b04868268ba2d6b57f6372e01558af), saved and not ledgered, since the record and the card cite v2",
    "files": {
     "html": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B5.pdf",
     "txt": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B5.txt"
    },
    "http": 200,
    "sha256_html": "1d063c0a3ec8619289b129db4ac36a221abc0779e6f078311f14a42a35f092ab",
    "surfaced_by_T": false,
    "admitted": "per the operator's ruling of 2026-10-29 (the /non rows are built 'as against their wikipedia articles if they exist, their own pages, etc.'): the operator's own paper, which the MMRS record cites (arxiv.org/abs/2411.15218, basis 'operator'); its abstract page is the card B1, its full text not surfaced by T",
    "names_operator": "yes ('Alex Glynn, MA'; 'I maintain a repository of examples known as Academ-AI')",
    "names_archive": "no",
    "note": "one work with B1 (§3.6: instances of one work are one lineage); the full text supplies the defined terms, criteria, results and stated limits the abstract summarises"
   },
   {
    "id": "B6",
    "origin": "own_page",
    "entity": "academ-ai",
    "url": "https://www.academ-ai.info/",
    "kind": "the entity's own site, home page (301 to https://academ-ai.info/; a Nuxt page whose list of examples loads client-side)",
    "fetched": "fetched 2026-10-29 at 05:06:04 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 68d32fe5488719b025749aed2fda708e932e355f203782974c3af2a3869a34ba, text sha256 2830daf183daabbbdce78f776762f0ba9bd1c18107acb3b8e45b614376b2430c after HTTP 301 from www to the bare domain (B6.headers.txt). The page served to the shell carries the site's heading and navigation; its examples are not in the served HTML. robots.txt fetched at 05:06:05 UTC, HTTP 200 (B6-robots.txt, sha256 0563ce67fbc4e4d58b49682897c6d74c272618c27503668fb7cbb32ee9f2ea36): 'User-agent: *' with 'Content-Signal: search=yes,ai-train=no,use=reference' and 'Allow: /'; Disallow for named crawlers and agents, among them ClaudeBot, Claude-User, Claude-Web, anthropic-ai, GPTBot, ChatGPT-User, Google-Extended, GoogleOther, Google-CloudVertexBot, Google-Agent, Perplexity-User; Googlebot is not named",
    "files": {
     "html": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B6.html",
     "txt": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B6.txt",
     "robots": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B6-robots.txt"
    },
    "http": 200,
    "sha256_html": "68d32fe5488719b025749aed2fda708e932e355f203782974c3af2a3869a34ba",
    "surfaced_by_T": false,
    "admitted": "per the operator's ruling of 2026-10-29: the entity's own address (the MMRS record's url field); not surfaced by T",
    "names_operator": "no",
    "names_archive": "no",
    "note": "The MMRS record observed this address on 2026-10-28 as 'blocked (robots.txt disallows our fetcher)', and the summarising fetcher was refused again on 2026-10-29; the shell fetch, a generic user-agent, was answered 200. The refusal is robots.txt's to the named agents; the site serves its pages to a generic client."
   },
   {
    "id": "B7",
    "origin": "own_page",
    "entity": "academ-ai",
    "url": "https://www.academ-ai.info/about",
    "kind": "the entity's own site, About page (reached from the home page's navigation: 'About')",
    "fetched": "fetched 2026-10-29 at 05:06:45 UTC with curl -sSL -m 30 -A 'Mozilla/5.0'; HTTP 200; html sha256 4f20561a8b120e3a9645eef152cdd3349e9406495bee3f060a0c2c5e5fbf0188, text sha256 2bd6b548492bc40c0b1a424d3bbc0c0c1d9318631d8b22bff2fae7317afc5aae (after 301 to https://academ-ai.info/about)",
    "files": {
     "html": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B7.html",
     "txt": "datasets/negative-of-the-negative/v2/intake/academ-ai-20261029/B7.txt"
    },
    "http": 200,
    "sha256_html": "4f20561a8b120e3a9645eef152cdd3349e9406495bee3f060a0c2c5e5fbf0188",
    "surfaced_by_T": false,
    "admitted": "per the operator's ruling of 2026-10-29 ('their own pages, etc.'): the entity's own self-definition, linked from its home page; the MMRS record does not cite it by address (eDiscovery Today, which the record cites, reports it: 'The About page on Academ-AI discusses the parameters of the site'); not surfaced by T",
    "names_operator": "partly: first person throughout, and the self-citation '(Glynn, 2024)'; the contact address is obfuscated in the served HTML",
    "names_archive": "no",
    "note": "the About page's scope (2026-10-29) includes 'dissertations/theses'; eDiscovery Today (B8, 2025-08-27) reported theses out of scope, and the MMRS record's intake follows B8"
   }
  ],
  "mmrs_sources": [
   {
    "id": "B8",
    "origin": "mmrs_source",
    "entity": "academ-ai",
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