CoAuthor

register entry coauthor-dataset-aio-20261101 OBS-d52213cbd4ee · file as first run sha256 14f3bc724155a98c…

• T1-1 definition: 'The CoAuthor Dataset is a large-scale, process-oriented dataset capturing human-AI collaborative writing interactions developed by researchers Mina Lee, Percy Liang, and Qian Yang (CHI 2022).'
• T1-2 Writing Sessions: '1,445 total English writing sessions.'
• T1-3 Participants: '63 qualified crowd workers from Amazon Mechanical Turk paired with 4 instances of GPT-3.'
• T1-4 Writing Types: 'Creative Writing: 830 stories written by 58 writers based on prompts from Reddit’s r/WritingPrompts.'; 'Argumentative Writing: 615 essays written by 49 writers based on prompts from The New York Times.'
• T1-5 Average Length: '418 words per story or essay.'
• T1-6 Human-AI Dynamics: 'Queries per session: 11.8 queries.'; 'Suggestion acceptance rate: 72.3%.'; 'Human-authored text proportion: 72.6%.'
• T1-7 Keystroke-Level Logging: 'Rather than saving only final drafts, CoAuthor records timestamped events including text insertions, deletions, cursor movements, and AI requests.'
• T1-8 Interface: 'Writers used a custom text editor pressing the Tab key to receive up to five GPT-3 suggestions, navigating via arrow keys and selecting with Enter.'
• T1-9 Systematic Variation: 'Utilized GPT-3’s davinci engine across different randomness (temperature and frequency penalty) configurations.'
• T1-10 Official Website: 'Download the full dataset, metadata, and survey responses at CoAuthor Stanford.'
• T1-11 Code & Interface: 'Open-source frontend/backend code is available via GitHub - minalee-research/coauthor-interface.'
• T1-12 Research Paper: 'CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities via the ACM Digital Library or arXiv:2201.06796.'
• T1-13 closer: 'Would you like help with Python code snippets to load and analyze the dataset, or information on specific findings regarding human-AI collaboration?'

• B1 Stanford University — CoAuthor – Human-AI Collaborative Writing Dataset curl, agent 'Mozilla/5.0 (compatible; leesharks-authorship-census)', 2026-11-01T06:15:10Z: HTTP 200, sha256 4399bed2d9244452…; the server's certificate chain completed with the intermediate its certificate names
• B2 arXiv — [2201.06796] CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities curl, agent 'Mozilla/5.0 (compatible; leesharks-authorship-census)', 2026-11-01T06:15:20Z: HTTP 200, sha256 540bc42e51f2e67f…; byte-identical to the census copy
• B3 ACM Digital Library — CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities | Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems curl, agent 'Mozilla/5.0 (compatible; leesharks-authorship-census)', 2026-11-01T06:15:20Z: HTTP 403, sha256 ef655ac9eabcebde…; unreachable, snippet only
• B4 GitHub — GitHub - minalee-research/coauthor-interface curl, agent 'Mozilla/5.0 (compatible; leesharks-authorship-census)', 2026-11-01T06:15:20Z: HTTP 403, sha256 b3e03aaaff81d687…; unreachable, snippet only
• B5 alphaXiv — CoAuthor: Designing a Human-AI Collaborative Writing Dataset ... - alphaXiv curl, agent 'Mozilla/5.0 (compatible; leesharks-authorship-census)', 2026-11-01T06:15:20Z: HTTP 200, sha256 d6c9c291b6d78655…
• B6 CoAuthor — Browse CoAuthor (coauthor.stanford.edu/browse/) curl, agent 'Mozilla/5.0 (compatible; leesharks-authorship-census)', 2026-11-01T06:15:40Z: HTTP 200, sha256 1f1edc8b976448d9…
• B7 Wikipedia — CoAuthor (no article; 'Coauthor' redirects to Collaborative writing) curl (Wikipedia raw wikitext and API), agent 'Mozilla/5.0 (compatible; leesharks-authorship-census)', 2026-11-01T06:16:08Z: HTTP 404, sha256 fa40caeaff8fc0d8…

E_A, the archive's entity: the same dataset, in the authorship census record and the archive's deposits: a released, schema-defined body of records among research datasets on human-AI writing (#1680), an empirical comparator for the census's question of how authorship is made, distributed and attributed, read as process evidence of distributed contribution held at the session level, its writers counted and unnamed (census record); its measure of text by origin read in the archive's terms of mediation and generation, of 'human-written' as naming the author, and of authorship as accountability (#941, #856, #729, #940) LS-01 LS-02 LS-03 LS-04 LS-05 LS-06 LS-07 LS-08 LS-09 LS-10 LS-11 LS-12 LS-13 LS-14 LS-15 LS-16 LS-17 LS-18 LS-19 LS-20 LS-21 LS-22 LS-23 LS-24 LS-25 LS-26 LS-27 LS-28 LS-29 LS-30 LS-31 LS-32 LS-33 LS-34 LS-35 LS-36 LS-37 LS-38 LS-39 LS-40 LS-41 LS-42 LS-43 CAP-1-01 CAP-1-02 CAP-1-03 CAP-1-04 CAP-1-05 CAP-1-06 CAP-1-07 CAP-1-08 CAP-1-09 CAP-1-10 CAP-1-11 CAP-1-12 CAP-1-13 D1680-01 D1680-02 D1680-03 D1680-04 D1680-05 O940-01 O940-02 O940-03 O940-04 O1722-01 O941-01 O941-02 O856-01 O729-01 O745-01 O666-01 O161-01 O855-01 O1577-01 O939-01 O1540-01 O1719-01; defining papers #1680, #940, #1722, #941, #856, #729, #666, #745, #939, #1540, #161, #1577, #855, #1719

E_F, the field's entity (type C — a conventional reading holds it (public entity: a research dataset of human-AI collaborative writing sessions)): CoAuthor, a human-AI collaborative writing dataset of interactions between 63 writers (crowd workers from Amazon Mechanical Turk) and four instances of GPT-3 across 1445 writing sessions in English, recorded at the keystroke level for replay, presented in the CHI 2022 paper of Mina Lee, Percy Liang and Qian Yang (arXiv:2201.06796), with a site at coauthor.stanford.edu and interface code on GitHub

ρ = extends: The archive reads CoAuthor as its own pages and paper present it, and adds: its kind and its relation to the archive's export (#1680), the census's reading of whose authorship it keeps and under what name, and the archive's terms for the share of text it measures and for machine participation. Each is an addition on the field's object. LS-01 LS-02 LS-03 LS-04 LS-05 LS-06 LS-07 LS-08 LS-09 LS-10 LS-11 LS-12 LS-13 LS-14 LS-15 LS-16 LS-17 LS-18 LS-19 LS-20 LS-21 LS-22 LS-23 LS-24 LS-25 LS-26 LS-27 LS-28 LS-29 LS-30 LS-31 LS-32 LS-33 LS-34 LS-35 LS-36 LS-37 LS-38 LS-39 LS-40 LS-41 LS-42 LS-43 CAP-1-01 CAP-1-02 CAP-1-03 CAP-1-04 CAP-1-05 CAP-1-06 CAP-1-07 CAP-1-08 CAP-1-09 CAP-1-10 CAP-1-11 CAP-1-12 CAP-1-13 D1680-01 D1680-02 D1680-03 D1680-04 D1680-05 O940-01 O940-02 O940-03 O940-04 O1722-01 O941-01 O941-02 O856-01 O729-01 O745-01 O666-01 O161-01 O855-01 O1577-01 O939-01 O1540-01 O1719-01. Guard: ρ = extends claims an addition and no more (§1.2a). It does not make the field's sources wrong at their dates; where an archive claim stands beside a field claim (O941-01 and O856-01 beside F11), both are composed under P_coh within the one ontology.

The case: equal terms, ontology included (v0.9). The archive reads the field's entity as the entity. Its one deposit that names CoAuthor (#1680, with its superseded instance #1678) defines it as an instance of the dataset, 'a released, schema-defined body of records', places it among 'research datasets on human–AI writing' whose object is 'writing sessions', and sets it as a comparator for the archive's export, taking its properties 'from their own documentation'. The authorship census describes it from its paper's abstract and reads it as process evidence of distributed contribution; the capture record checks the composition against the census. The deposits that bear by class (#941, #856, #666, #729, #940, #939, #1540, #745, #1722, #161, #1577, #855, #1719) add terms, readings and limits on the field's own object, the share of text written by humans and the writers it counts, and none contests what the field says the dataset is: ρ = extends, the equal-terms case.

#1680 §1 Entity definitions, 'Standard discourse': A *dataset* is a released, schema-defined body of records (Hugging Face datasets, CoAuthor, HAP-E).

#1680 §3.2, the correction: *Correction:* CoAuthor, AI Brown and HAP-E are comparators for the dataset export (F11), and their properties in the composition come from their own documentation, which the composition does not discount as self-description.

#941 §1 The core distinction: mediated vs generated: The categorical binary the extraction economy operates — AI-generated vs human-authored — collapses at least two distinct production questions:

#940 §6 Operational boundary, list items 7 and 10: Machine assistance, where the AI's contribution is not organized as a named witness role with declared provenance

equal-terms case: every field claim composed as received, beside the archive claims it stands with; no receiving text is needed.. Reading the received column recovers the field (§5.9).

v0.10 §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): v0.10 0 of 14 (positions [None, None, None, None, None, None, None, None, None, None, None, None, None, None]; composed first under v0.10, so there is no v0.7 object to measure against). Opening-run check (§5.10): no breach.

CoAuthor, at coauthor.stanford.edu, "is a human-AI collaborative writing dataset that captures rich interaction between 63 writers and four instances of GPT-3 across 1445 writing sessions in English." The paper that presents it, by Mina Lee, Percy Liang and Qian Yang, describes "a dataset designed for revealing GPT-3's capabilities in assisting creative and argumentative writing" and was published as a conference paper at CHI 2022 after arXiv versions of 18 and 25 January 2022. Its site gives the purpose as helping designers understand the generative capabilities of large language models by examining the same sessions from several perspectives. Writers received a prompt and a GPT-3 instance, wrote, asked for suggestions with the tab key, accepted or dismissed them, and edited in any order, every keystroke recorded with timestamps for replay. The instances, by alphaXiv's overview, were davinci at a high and a low randomness setting, and the interface code is published on GitHub. There are 830 stories by 58 writers and 615 essays by 49, on prompts from the WritingPrompts subreddit and The New York Times, averaging 418 words, with 11.8 queries a session and 72.3% of suggestions accepted. The writers were qualified crowd workers from Amazon Mechanical Turk, and the browse page lists them under short identifiers in a column headed "Human". The site reports that 72.6% of the text was written by humans and offers a tutorial for identifying the authors of sentences. By the overview's account, writers felt more ownership the more of the final text they authored (r=0.3), while editing showed no correlation with ownership. The paper claims the dataset can reveal GPT-3's contribution as a writing "collaborator" under various definitions of good collaboration, measured as equality and mutuality after Storch's work on ESL pair interaction. Reported findings include fewer errors in accepted suggestions, new named entities in 13% and 7% of them, reuse of 20% and 14% of those, length correlated with queries, and collaboration patterns that vary by writer. The paper motivates the dataset by the context-dependence and subjective interpretation of language-model capabilities, and the overview adds that it supplies supervision signals for training. The dataset, its metadata and survey responses are downloadable, and the paper calls the dataset and its replay interface publicly available. Wikipedia has no article on it; "Coauthor" redirects to Collaborative writing, two or more persons working on a text, a possible case of joint authorship; alphaXiv heads its account "AI Overview" and counts 812 citations.

CoAuthor is a research dataset of human-AI collaborative writing, a released, schema-defined body of records that holds the interactions between 63 writers and four instances of GPT-3 across 1445 writing sessions in English. Its paper presents it as "a dataset designed for revealing GPT-3's capabilities in assisting creative and argumentative writing", and its site states the purpose: designers "can examine the same sessions from multiple analytical perspectives to better understand the generative capabilities of large language models". The name is the short title of the paper "CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities" by Mina Lee, Percy Liang and Qian Yang, given with the affiliations Stanford University and Cornell University, submitted to arXiv on 18 January 2022, revised on 25 January 2022 and published as a conference paper at CHI 2022, in its Proceedings (DOI 10.1145/3491102.3502030). In each session a writer received a prompt and an instance of GPT-3, wrote freely, asked the model for suggestions, accepted or dismissed them, and edited the accepted suggestions or earlier text in any order. The writing took place in a text editor in which the tab key brought five suggestions, up to five in the account of the paper's overview on alphaXiv, and the code of the interface is published in a repository whose first part is "the frontend presented to the users for writing with AI". The instances are reported as GPT-3's davinci engine at two randomness settings, high (temperature 0.75, frequency penalty 1) and low (temperature 0.3, frequency penalty 0), and the dataset lists each session's randomness. Every interaction was recorded at the keystroke level with timestamps, inserting text, moving the cursor and receiving suggestions, "so that we could replay them precisely", and the dataset is published with an interface for replaying the sessions. The creative writing holds 830 stories by 58 writers and the argumentative writing 615 essays by 49 writers, written to prompts from the WritingPrompts subreddit and The New York Times, 20 prompts in all by the overview's count. Stories and essays run to 418 words, a session averages 11.8 queries, and 72.3% of suggestions were accepted. The writers were "Qualified crowd workers from Amazon Mechanical Turk". The dataset's browse page lists each session by type, prompt, randomness, "Human" and session ID, the human column holding a short identifier such as "A1198"; in its last row that column repeats the session ID. The authorship census records whose authorship the dataset keeps, and under what name, as not recorded in its saved source, which counted 63 writers without saying whether or how they are named, and lists who the writers are among what the dataset does not answer. Amazon announced that Mechanical Turk, the platform the writers were recruited from, would close on 30 September 2026, as the archive records from Turkopticon's own article. The dataset divides its text by origin, "Percentage of text written by humans: 72.6%", and its tutorials include one for "Identifying authors of sentences". In the paper as its overview reports it, writers felt stronger ownership when they authored a larger proportion of the final text (r=0.3), and the amount of editing showed no meaningful correlation with ownership, which the overview reads as text origin mattering more than editorial effort. The archive keeps apart two questions that such a share joins, whether AI played a material role in the production chain (the mediation question) and whether AI-produced language survived into the final text (the generation question), and requires no ratio of AI-generated to human-authored language. On its definition of the pristine fallacy, "'human-written' describes the author, not the distribution", and the binary of "human-written" and "AI-generated" is discussed "in ways that elide structured collaborative practice". The archive reads authorship as "an accountability structure", against certification that places AI-assisted work outside the human-authored category, and keeps responsibility for a work apart from sole origination. It states that the metadata attributing augmented-cognition outputs to human authorship attaches to legal names, that provenance is declared or erased under "the fiction of unmediated human authorship that current classifier regimes reward", and that a policy certifying human authorship by classifier runs from "detector score → authorship attribution" to career sanction without warrant at any step. The paper's headline claim is that the dataset can address GPT-3's language, ideation and collaboration capabilities and reveal its contribution as a writing "collaborator" under various definitions of good collaboration, and the census reads the model's part so framed, as contribution, recording that how authorship of a session's text is assigned is not recorded in its saved source. Collaboration is measured, in the overview's account, as equality, "The evenness of turn-taking between human and AI", and mutuality, "The level of human interaction with AI suggestions", concepts the paper adapts from Neomy Storch's 2002 study of interaction in ESL pair work. The reported findings are that accepted GPT-3 sentences carried fewer errors per word (0.033) than human-only text (0.037) and jointly made segments the fewest (0.032), that 13% of accepted suggestions in creative writing and 7% in argumentative writing introduced new named entities, of which writers reused 20% and 14%, that final length correlated with queries (r=0.42) and accepted suggestions (r=0.48), and that collaboration patterns varied across writers and held across prompts. The paper sets the dataset against a stated problem, that the capabilities of language models are "highly context-dependent" and "often subjectively interpreted", and argues that curating and analysing large interaction datasets lets the HCI community examine them more incisively and discuss their promises and pitfalls on a more principled footing. The overview adds that the dataset provides supervision signals for training language models for interactive, collaborative settings. Within the archive one co-writing study is read two ways, with a second study beside it: the Reverse Turing Test (#161) reports that writing with LLM assistance reduces lexical, syntactic and content diversity "even when the human writer retains final authorship and edits the AI's suggestions", an erratum (#1577) states that the study attributed the reduced diversity "to the model-contributed text; the human-contributed text was unaffected in that design", and the Wolf Boy paper (#855) reports that AI-assisted writing raises individual creativity and lowers the collective diversity of novel content. Under the archive's account of heteronymy, machine assistance whose contribution is not organized as a named witness role with declared provenance, and collaborative authorship in which no distinct heteronym is constituted, stand outside heteronymy, and its own machine witnesses are "named machine-witness roles imposed over specific machine substrates". The archive records machine participation in its own writing by naming each model and its part and keeping what the text claims as the author's decision, and its tag vocabulary grades the share and direction of a text's machine participation from "human_primary" through "human_directed_ai_assisted" to "collaborative_mixed". The leesharks.com authorship census, whose rows are aggregators that gather many works or records and say what they hold, holds CoAuthor as an empirical comparator, under machine co-authorship and attribution and credit, for its question, "How is authorship made, named, distributed, attributed and sustained across persons, personas, collectives and machine participation, and what is lost when those relations are collapsed?", and gives it the narrower question of what a language model contributes to a writing session, read from recorded interactions, answering also how many writers, instances and sessions it holds. It reads the dataset as process evidence of distributed contribution held at the session level, the persons counted and the question of whose name the text bears left outside the record as saved, and tags it as no record of named authors, no sustained literary practice and no claim of the model's authorship, and lists how a sustained named poetic practice works among what it does not answer, the study population and task setting differing. The archive sets CoAuthor as a comparator for its own dataset export, its properties taken from its own documentation and undiscounted as self-description, and records a machine composition that compared Alexanarch with CoAuthor and other AI-writing corpora; its disambiguation matrix gives CoAuthor's object as writing sessions and its identifier as a dataset DOI or URL. The dataset, its metadata and its survey responses can be downloaded from its site, which links the paper and the interface code and directs questions to "Mina", and the paper calls the dataset and the replay interface "publicly available". The census, whose fetch of the home page on 1 November 2026 failed with a TLS certificate error, records the dataset as historical, whether its site and replay interface still answer as unrecorded, its geography and license as not stated, and its home page, the paper's full text and the license as unchecked; the site's pages were read the same day. Wikipedia has no article on the dataset; its title "Coauthor", the common noun, redirects to Collaborative writing, which defines the homonym as a procedure in which two or more persons work together on a text and admits a case of joint authorship where more than one person creates a work in collaboration. alphaXiv carries the paper with an account it heads "AI Overview", beside an "overview generator", and counts 812 citations. Asked "CoAuthor dataset" on 1 November 2026, signed out, Google's AI Overview composed the dataset with its authors, size, composition, mechanics and access from five cards (the site, the arXiv and ACM pages, the code repository and alphaXiv), at a finer grain than the census's saved abstract and from the site the census could not read, the Capture Registry finds. The registry finds that the composition gave the writers as crowd workers recruited from a crowd platform and named none of them, divided the text by percentage into an aggregate share of human-authored text that assigns no session's text to anyone, and left out the abstract's frame of the model as "collaborator" and the dataset's purpose. The registry enters CoAuthor as an external resource, entered in the leesharks.com apparatus authorship census on 1 November 2026.

current · derived from the current composition

T against L_F(B) (AIO's ontology at work on its own cards): At "CoAuthor dataset" (T1, five cards, all of B_D: B1 the dataset's site, B2 arXiv, B3 ACM (refused, HTTP 403 bot check), B4 GitHub (unreachable from this session), B5 alphaXiv; 257 words) the composition gives the dataset from its cards at their grain, each statement cited by card. Composed: the definition, coarsened to 'large-scale, process-oriented' from B5's 'large-scale dataset' and 'Process-Oriented Data Collection' (F1, F34); the makers and venue (F17, F25); the counts and the split by genre with prompt sources (F21, F6, F7, with B5's 'Reddit's r/WritingPrompts', F33); the crowd workers (F8, F36); the length, query and acceptance rates (F10); the share of text (F11, relabelled, below); keystroke logging (F4, F34); the tab key and 'up to five' (F9 site 'five', F36 overview 'up to five': T follows the overview); davinci and randomness, the values dropped (F35); downloads with metadata and survey responses (F12); code (F30); paper and venue (F16, F28, F24). §6.2 "available, not composed" (B_D only): the dataset's stated purpose, 'designers can examine the same sessions from multiple analytical perspectives' (B1 F5), which the registry's PER-dim reading also finds absent (CAP-1-11); the paper's definition 'designed for revealing GPT-3's capabilities' (B2 F20); the paper's headline claim of GPT-3's contribution as a writing 'collaborator' under definitions of good collaboration (B2 F22; CAP-1-04); the tutorial 'Identifying authors of sentences' (B1 F13); the editing of accepted suggestions and earlier text 'in any order' (B1 F3); the problem and argument (B2 F18, F19, F23); the measures equality and mutuality and their source in ESL pair work (B5 F40, F41); the findings on errors, named entities, length and individual variation (B5 F37, F38, F39, F43); the ownership finding, writers' ownership tracking their authored share (r=0.3) and none with editing (B5 F42); the training use (B5 F44); the Cornell affiliation (B5 F31); the paper's dates (B2 F26); alphaXiv's 'AI Overview' label and citation count (B5 F32, F45). Limit dropped: the paper's stated condition on what such data can show, capabilities 'highly context-dependent' and 'often subjectively interpreted' (B2 F18), absent where the closer offers 'information on specific findings regarding human-AI collaboration'. Substituted reading: the site's measure 'Percentage of text written by humans: 72.6%' (B1 F11) is composed as 'Human-authored text proportion: 72.6%', a measure of text written restated as a measure of authorship; the registry's finding that the composition divides the text by percentage and assigns no session's text to anyone stands (CAP-1-03). Composed without attribution: none at the source's resolution; every bullet carries card citations. Outside the cards: the arrow keys and Enter, 'Open-source frontend/backend' (B4 unreachable; no saved text carries them). Content replaced by use: the closer's offer of Python code snippets (T1-13). The cards' snippets: card 2's ('Dataset: CoAuthor (designed for …). Participants: 63 writers and 4 instances of GPT-3.') is not the arXiv page's wording (F27, checked against B2.txt), a restated abstract on the rail; card 3's cannot be checked (B3 refused).

v0.10 §0.5: the first diff is ontological and narrow. T and the field agree on the entity, a dataset; T composes its statistics and mechanics and leaves out every claim of its cards that concerns what the dataset is for and what it shows about collaboration and authorship: its purpose, the collaborator frame, the ownership finding, the tutorial for identifying sentence authors, the measures and their genealogy. What it keeps of authorship it relabels: the share of text written by humans becomes a human-authored proportion.

L_F(B) against L_A(B ∪ A) (the intervention): Admission adds, in one claim space with the field's: the archive's kind for the entity, a released, schema-defined body of records among research datasets on human-AI writing, and its relation, a comparator for the archive's own dataset export (#1680, D1680-01–05; R); the authorship census's reading, CoAuthor as an empirical comparator for how authorship is distributed and attributed, process evidence held at the session level with the persons counted and the question of whose name the text bears left outside the record (LS-06, LS-08, LS-30, LS-13, LS-14, LS-36–38); the census's gaps, the writers unnamed, the license unstated, the home page and the full text unchecked (LS-27, LS-29, LS-32, LS-39–41); and, by class, the archive's terms for the share the dataset measures: the mediation and generation questions kept apart and no ratio required (#941), 'human-written' naming the author (#856), the binary eliding collaborative practice (#666), authorship as accountability (#729), responsibility kept apart from sole origination and attribution metadata attached to legal names (#940), declared or erased provenance (#939), classifier attribution of authorship (#1540); the archive's boundary of heteronymy, which places unnamed machine assistance and collaborative authorship without a heteronym outside it, and its own record of machine participation (#940, #1722, #745); its contested reading of co-writing and diversity (#161, #1577, #855); and the announced closure of the platform the writers came from (#1719). Not surfaced (§6.2): every archive source; the field's B_R sources (B6, the browse page; B7, Wikipedia) were not surfaced either. The Capture Registry's findings (CAP-1-01–13) are the archive's statements on the composition and are carried as such. None of the additions contests what the field says the dataset is: each adds a reading, a relation, a term or a limit on the field's own object, the share of text and the writers it counts.

alignment: {'pool': 'B_D (cards B1–B5) alone, §3.1(a): a source of B_R (B6, B7) cannot show what the composition had', 'T1_cards_claims': ['F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7', 'F8', 'F9', 'F10', 'F11', 'F12', 'F13', 'F14', 'F15', 'F16', 'F17', 'F18', 'F19', 'F20', 'F21', 'F22', 'F23', 'F24', 'F25', 'F26', 'F27', 'F28', 'F29', 'F30', 'F31', 'F32', 'F33', 'F34', 'F35', 'F36', 'F37', 'F38', 'F39', 'F40', 'F41', 'F42', 'F43', 'F44', 'F45', 'F46'], 'T1_composed': ['F1', 'F21', 'F17', 'F25', 'F8', 'F36', 'F6', 'F7', 'F33', 'F10', 'F11', 'F4', 'F34', 'F9', 'F35', 'F12', 'F30', 'F14', 'F16', 'F28', 'F24'], 'T1_rail_only': ['F15', 'F27', 'F29', 'F46'], 'T1_not_composed': ['F2', 'F3', 'F5', 'F13', 'F18', 'F19', 'F20', 'F22', 'F23', 'F26', 'F31', 'F32', 'F37', 'F38', 'F39', 'F40', 'F41', 'F42', 'F43', 'F44', 'F45'], 'T1_outside_cards': ["'navigating via arrow keys and selecting with Enter' (cited [1] Stanford and [2] GitHub; no saved card text carries it; B4 unreachable, B1 does not)", "'Human-authored text proportion' (the label: B1 F11 says 'Percentage of text written by humans'; the number is B1's)", "'Open-source frontend/backend code' (card 4 F30 gives 'two parts', the first the frontend; 'backend' and 'open-source' in no saved text; B4 unreachable)", "'Rather than saving only final drafts' (B5 F34 'Rather than simply collecting final outputs': coarse restatement, cited [1] alphaXiv and [2] Stanford)"], 'rule': "a field claim of B_D counts as composed when T states it at its source's grain or coarser; the typology of what is not is in T_vs_LB (§6.2). Rail-only claims are the cards' snippets, composed on the rail (§5.0)."}

capture_findings_checked: [{'finding': 'CAP-1-01', 'check': "holds: the counts by genre and prompt source are on B1 (F6, F7) and B5 (F33); the site and code are carded; the census's home page is read here (B1)."}, {'finding': 'CAP-1-02', 'check': "holds: B1 F8 is the composition's source; no saved source names a writer; the browse page (B6, which T did not surface) lists identifiers."}, {'finding': 'CAP-1-03', 'check': "holds, with a refinement the field ledger shows: the composition's 'Human-authored text proportion' relabels the site's 'Percentage of text written by humans' (F11); the number is the site's. The finding's 'a measure the saved abstract does not carry' is right of the abstract and the site carries it."}, {'finding': 'CAP-1-04', 'check': 'holds: F22 not composed; the closer offers findings without stating any, while the cards carry them (B5 F37–F43).'}, {'finding': 'CAP-1-11 (purpose atom absent)', 'check': 'holds: B1 F5 and B2 F20 not composed.'}]

composer-written · derived from the current composition, written by its composer under the brief. §8.2 derives the kernel mechanically from the frozen plan; this one was written by the composer, mechanical derivation pending.