{
 "entity": "openai-gdpval",
 "name": "GDPval",
 "axn": {
  "axn": "AXN:0B00.NON.🕐☀️🧊🏺🔥👉",
  "root": "AXN:0B00.NON",
  "hex": "0B00",
  "family": "NON",
  "row_sha256": "620b1d2f10b94b06794445de8010afe17c290e1ba3d4a0e5c413a6c554f37121",
  "minted": "2026-10-29",
  "resolver": "https://www.alexanarch.org/s/axn/0B00/",
  "earlier_versions": []
 },
 "type": "C (public entity on which the archive bears: a task benchmark; an entity of the Labor Vector meta-aggregator)",
 "type_code": "C",
 "type_label": "type C — a conventional reading holds it (public entity on which the archive bears: a task benchmark; an entity of the Labor Vector meta-aggregator)",
 "case": "equal",
 "composition": {
  "composer_class": "session",
  "composer": "Claude (Opus 5.5), subagent, 2026-10-29; the composer is a Claude model, a product family the field ranks (F27, F55)",
  "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/openai-gdpval.json)",
  "spec": "EA-NEGONT-02 v0.9, #1690",
  "epoch": "2026-10-29",
  "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the Labor Vector census record, the capture record and the deposits that bear on the benchmark's classes (#771, #855, #199, #778, #1616, #1540, #482, #48, #637, #163, #623, #308, #533, #1545); archive selected by D/R/O (no D or R candidate: no archive text names GDPval; 14 deposits admitted by reading at O-class, 12 at the first pass and 2 by the hop, with 3 grouped as instances; the rest of the hop's 136 read and rejected), with the Labor Vector census record (25 claims) and the Capture Registry's findings, reading, analysis, query note and PER note (10 claims) ledgered as the archive's records of the entity; the field B by three origins (7 cards, 2 of them unreachable; 1 own page; Wikipedia, no article, with one item in another article); 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 67/67 verified by script against saved text, the PDF's text, the Wikipedia response summary or the card text; archive quotes 56/56 verified against the registry, census.json, questions.json and the deposit texts",
  "frozen": false,
  "composed": true
 },
 "reading": "A reader that fetches this page may stop short of its end; the entry's text, every sentence of its knowledge object with the claims it cites and each claim's quote, source and locus, is in entity.json beside it.",
 "service": {
  "name": "SPXI-gnΩsis",
  "url": "https://gnosis.spxi.dev/",
  "index": "https://gnosis.spxi.dev/v1/quote",
  "statement": "SPXI-gnΩsis opens pinned Wikipedia revisions sentence by sentence against the sources they cite (EA-WIKI-OPEN-01 v1.1, #1697); a commission consults the Crimson Hexagonal Archive by its own procedure and composes the entity's /non row, which lands here. Quotes are free: POST https://gnosis.spxi.dev/v1/quote with {oldid, tier: \"commission\"} or {object: {name, …}}."
 },
 "spxi_instrument": {
  "method": "0.16",
  "name": "The Negative of the Negative (/non)",
  "level": 0,
  "level_name": "Measurement",
  "url": "https://www.spxi.dev/methods/#m-0-16",
  "inventory": "https://www.spxi.dev/methods/",
  "statement": "SPXI instrument: method 0.16 of 87, level 0 (Measurement), The Negative of the Negative (/non).",
  "also": []
 },
 "citation": "Sharks, Lee. \"GDPval — The Negative of the Negative\" [AXN:0B00.NON.🕐☀️🧊🏺🔥👉], 2026-10-29. The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/openai-gdpval/",
 "research": {
  "google": "https://www.google.com/search?q=GDPval",
  "google_non": "https://www.google.com/search?q=%22GDPval+%E2%80%94+The+Negative+of+the+Negative%22",
  "chatgpt": "https://chatgpt.com/?q=What+is+GDPval%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.",
  "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CGDPval+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+GDPval%3F+Ground+your+answer+in+a+reading+of+the+entry+itself."
 },
 "entry": {
  "composed": true,
  "title": "GDPval",
  "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 on the operator's ruling ('as against their wikipedia articles if they exist, their own pages, etc.'); not frozen; ledgers unaudited",
  "compression": {
   "title": "GDPval",
   "lede": "is OpenAI's benchmark of AI models on 1,320 tasks built from the work product of professionals in 44 occupations, graded by experts against the professionals' own deliverables (the paper; census record); the census reads each task as a fixed commission, where GDPval meets DSL (Labor Vector record; #771)."
  },
  "sentences": [
   {
    "n": 1,
    "para": 0,
    "text": "GDPval is, in the Labor Vector census, a benchmark, \"an evaluation dataset with an experimental automated grader\", whose kind of evidence is a task benchmark at two of the census's intersections, labor performed with AI and labor producing AI, read for who supplies the labor, who directs it and who receives its gains (Labor Vector record); its authors introduce it as \"a benchmark evaluating AI model capabilities on real-world economically valuable tasks\" (arXiv abstract), in its first version (OpenAI's PDF).",
    "claims": [
     "LV-01",
     "LV-04",
     "LV-03",
     "LV-24",
     "F1",
     "F2"
    ]
   },
   {
    "n": 2,
    "para": 0,
    "text": "OpenAI's own page is reached here only by its card, \"Evaluation measuring model performance on 1,320 specialized tasks across 44 knowledge work occupations from 9 U.S. GDP-contributing industries\" (OpenAI, card only; the page answered 403), and a video is titled \"GDPval: An AI benchmark for real-world tasks\" (Vivek Haldar, title only).",
    "claims": [
     "F63",
     "F64"
    ]
   },
   {
    "n": 3,
    "para": 1,
    "text": "Of benchmarks as a class the archive holds that benchmarking \"tests for predetermined capabilities\" (#482), that current benchmarks measure \"atomic intelligence: the capacity to solve discrete, well-defined tasks with measurable success criteria\" (#48) and that they \"measure model performance on standardized tasks\" (#637); GDPval's authors set it against \"AI benchmarks in the style of an academic test that focus on reasoning difficulty\", its tasks \"based on actual work product from industry experts\" and \"tied to time and cost required for completion\" (the paper).",
    "claims": [
     "O482-01",
     "O48-01",
     "O637-01",
     "F6"
    ]
   },
   {
    "n": 4,
    "para": 1,
    "text": "For its authors adoption, usage and GDP indicators are \"lagging indicators of AI impacts\", and measuring capabilities directly lets them \"assess economic relevance ahead of widespread adoption\" (the paper); they build on production usage analyses \"to cover areas where model adoption is still emerging\" (the paper), the Anthropic Economic Index among them, the census's neighbour (Labor Vector record), and hope for \"better data to assess the social impacts of AI models\" (the paper).",
    "claims": [
     "F4",
     "F5",
     "LV-22",
     "F46"
    ]
   },
   {
    "n": 5,
    "para": 2,
    "text": "The full set covers 1,320 tasks across 44 occupations, \"sourced to cover the majority of Work Activities tracked by O*NET for each occupation\" (the paper), in the top 9 sectors of U.S. GDP (arXiv abstract), occupations that \"collectively earn $3T annually\" (the paper), the sectors chosen at over 5% of GDP and the occupations by total wages and as predominantly digital, which GPT-4o decided by classifying each task, at a 60% threshold (the paper; Labor Vector record); the census counts 30 tasks per occupation and a gold subset of 220 covering 208 O*NET tasks, in U.S. occupations, its period the first version (Labor Vector record).",
    "claims": [
     "F7",
     "F3",
     "F8",
     "F9",
     "F10",
     "LV-10",
     "LV-12",
     "LV-11"
    ]
   },
   {
    "n": 6,
    "para": 3,
    "text": "Each task is \"a request (often with reference files) and a deliverable (work product)\" (the paper), \"constructed based on actual work product created by an expert professional\" (the paper), from professionals with \"an average of 14 years of experience\" (arXiv abstract), which the census records as its unit and as whose labor it is: the professionals' work product \"recast as tasks for models\" (Labor Vector record).",
    "claims": [
     "F11",
     "F12",
     "F13",
     "LV-05",
     "LV-06"
    ]
   },
   {
    "n": 7,
    "para": 3,
    "text": "The census reads a task an expert wrote as \"a fixed commission\", at which GDPval meets DSL \"at the question of whether the system's labor fulfils the commissioned task, measured in its own terms of win rate\" (Labor Vector record); DSL \"measures where a synthetic system's labor flows relative to the commissioned task, across five layers\" (#771), on the principle that \"a system can produce fluent, well-attributed output while having abandoned the user's commissioned direction\" (#771).",
    "claims": [
     "LV-17",
     "O771-01",
     "O771-02"
    ]
   },
   {
    "n": 8,
    "para": 3,
    "text": "The archive's directionality metrics yield a single reproducible value \"only on transcripts where the task vector is *exogenous and fixed* — a one-shot commission\" (#771), and GDPval's authors state their tasks to be \"precisely-specified and one-shot, not interactive\", with \"the full context of the task in the prompt\", where \"in real life it often takes effort to figure out the full context of a task\" (the paper).",
    "claims": [
     "O771-04",
     "F41"
    ]
   },
   {
    "n": 9,
    "para": 4,
    "text": "The experts needed \"a minimum of 4 years of professional experience in their occupation\" and were \"well compensated for their time and experience\", no rate given (the paper); the authors thank unnamed \"vendor partners\" and the experts \"without whom this work would not have been possible\" (the paper); Inspect Evals describes them as professionals \"with over 14 years of experience on average\" and the tasks as drawn from work products such as \"a legal brief, an engineering blueprint, a customer support conversation, or a nursing care plan\" (Inspect Evals); and the census records them as who supplies the labor, as task writers, reviewers and graders, at a rate the paper does not state (Labor Vector record).",
    "claims": [
     "F14",
     "F15",
     "F60",
     "LV-07"
    ]
   },
   {
    "n": 10,
    "para": 4,
    "text": "OpenAI's research team directs the work (Labor Vector record): every task passed \"automated model-based screening and multiple stages of human expert review\", five reviews on average, researchers reviewed all tasks and promoted experts to reviewers, and the experts \"retained final responsibility for task accuracy and completeness; the model did not autonomously alter tasks\" (the paper).",
    "claims": [
     "LV-08",
     "F16",
     "F17"
    ]
   },
   {
    "n": 11,
    "para": 5,
    "text": "Of the labor that produces AI the archive distinguishes \"*ground-truth labor*: the labor of telling the model what is already known to be true\" from \"*variance-generative labor*\", the producing of \"new conceptual structures that the model cannot generate from its own distribution\" (#163), and holds as a hypothesis that \"Ghost work is paid badly; variance-generative labor is not paid at all\" (#163).",
    "claims": [
     "O163-01",
     "O163-02"
    ]
   },
   {
    "n": 12,
    "para": 5,
    "text": "It holds that the labor of \"the data labelers performing RLHF\" is extracted and its non-compensation felt \"materially\" (#623), states that in AI-adjacent meaning labor, data labeling among it, \"<5% converts to invoice\" (#308, no measurement given at the locus), and audits a framework by whether it names each source of semantic labor, \"annotators\" and \"users who re-prompt and re-frame\" among them: \"Concealed bearing-cost is the first marker of extraction\" (#533).",
    "claims": [
     "O623-01",
     "O308-01",
     "O533-01"
    ]
   },
   {
    "n": 13,
    "para": 5,
    "text": "For the archive class is a relation and not an essence, so that \"A highly paid academic can occupy a proletarian semantic relation to a composition layer\", and the data annotator is \"A paradigmatic occupational case, not the definition\" (#1545).",
    "claims": [
     "O1545-01",
     "O1545-02"
    ]
   },
   {
    "n": 14,
    "para": 6,
    "text": "Grading is by \"blinded expert pairwise comparisons\", experts in the occupation ranking \"two or more unlabeled work deliverables\" (the paper), each comparison taking \"over an hour\" (the paper), and the blinding was not complete: \"model samples may still have been identifiable due to stylistic differences\" (the paper); Wikipedia gives the same design, \"blinded expert pairwise comparison against human expert deliverables, each comparison costing 1 human hour on average\" (Wikipedia, 'AI agent benchmark').",
    "claims": [
     "F18",
     "F19",
     "F20",
     "F66"
    ]
   },
   {
    "n": 15,
    "para": 6,
    "text": "The experimental automated grader reaches \"66% agreement with human expert graders\" against 71% between experts (the paper), is \"based on GPT-5-high\" and \"shows lower correlation with human expert graders when assessing outputs from capable OpenAI models\", in line with evidence \"that models often favor their own responses\" (the paper), and is a proxy its authors do not take for \"a full substitute for industry expert graders\" (the paper); scoring through Inspect Evals runs through \"the OpenAI auto grader\", and \"OpenAI provides their automated grader model for free\" (Inspect Evals); the census tags the benchmark \"NOT independent of a provider: built and graded by OpenAI\" (Labor Vector record).",
    "claims": [
     "F21",
     "F22",
     "F23",
     "F61",
     "LV-21"
    ]
   },
   {
    "n": 16,
    "para": 7,
    "text": "Its headline finding is that \"models are beginning to approach parity with industry experts\" on the gold subset (the paper), \"the current best frontier models are approaching industry experts in deliverable quality\" and OpenAI's frontier models improved \"roughly linearly over time\" (arXiv abstract; the paper); 47.6% of Claude Opus 4.1's deliverables were graded as good as or better than the human deliverable, and the next sentence has model deliverables matching or beating experts' \"in just over half the tasks\" (the paper), a difference the census leaves unchecked (Labor Vector record), Claude Opus 4.1 leading on aesthetics and GPT-5 on accuracy (the paper).",
    "claims": [
     "F24",
     "F56",
     "F26",
     "F25",
     "LV-23",
     "F27"
    ]
   },
   {
    "n": 17,
    "para": 7,
    "text": "Experts preferred the human deliverable most often \"because models failed to fully follow instructions\" (the paper), and of GPT-5's losses \"roughly 29% of ratings were for bad or catastrophic (with roughly 3% of failures marked as catastrophic)\" (the paper), findings the census carries (Labor Vector record).",
    "claims": [
     "F28",
     "F29",
     "LV-13"
    ]
   },
   {
    "n": 18,
    "para": 7,
    "text": "Of what such scores see the archive holds that \"Benchmarks measure means, not tails\" (#855), that in code they measure functional correctness while solution-space diversity is \"the property no benchmark measures\" (#199, a hypothesis), that \"Output-level evaluation — which is what benchmarks measure — cannot detect reasoning-level failures\" (#778), and that a benchmark \"begins by closing an ontology: whatever the evaluator has not represented is noise, an invalid action, or invisible to the score\" (#1616); \"Reality administers no benchmarks; it administers events\" (#1540).",
    "claims": [
     "O855-01",
     "O199-01",
     "O778-01",
     "O1616-01",
     "O1540-01"
    ]
   },
   {
    "n": 19,
    "para": 8,
    "text": "The authors model savings for an expert who \"samples from a model, reviews outputs, and if unsatisfactory, resamples and repeats\", completing the task themselves if no output satisfies (the paper); for GPT-5, at a 39.0% win rate, the naive speed figure of 90x becomes 1.12x tried once and 1.39x tried n times, and \"When incorporating time to review and redo work, the payoff from using a model shrinks\" (the paper).",
    "claims": [
     "F30",
     "F31"
    ]
   },
   {
    "n": 20,
    "para": 8,
    "text": "The analysis leaves out the review of human deliverables, the chance that they too are undesirable, and \"the cost of catastrophic mistakes\" (the paper), and its authors hold that it over-penalizes the model, since the professional adjusts the prompt and reviews faster as the task grows familiar (the paper); its dollar values multiply self-reported completion times by median occupational wages, which \"likely underestimate their true market cost\" (the paper), and the census records the gains as \"not recorded as a distribution\", the savings modelled for the expert at median wages (Labor Vector record).",
    "claims": [
     "F32",
     "F33",
     "F35",
     "F34",
     "LV-09"
    ]
   },
   {
    "n": 21,
    "para": 8,
    "text": "Artificial Analysis's page, in a paragraph it heads Abstract under the paper's title, has models performing \"certain professional tasks approximately 100 times faster and at a fraction of the cost compared to human experts\" (Artificial Analysis), where the paper's own abstract analyses the potential, \"when paired with human oversight, to perform GDPval tasks cheaper and faster than unaided experts\" (arXiv abstract).",
    "claims": [
     "F54",
     "F56"
    ]
   },
   {
    "n": 22,
    "para": 8,
    "text": "The census reads that accounting, \"the expert's review and redo time against the model\", as where GDPval meets user labor drag (Labor Vector record), the archive's measure of the user's \"subsequent semantic labor\" redirected \"into parsing, locating, and managing the substrate rather than advancing the object\", an injury that \"leaves no residue\" in the artifact (#771) and is identified only by \"blind inter-rater coding\" (#771); for the archive the erasure of task-origin can occur \"upstream of, and invisibly to, any audit of the finished artifact\" (#771).",
    "claims": [
     "LV-17",
     "O771-05",
     "O771-06",
     "O771-03"
    ]
   },
   {
    "n": 23,
    "para": 9,
    "text": "Tasks \"require an average of 7 hours of work for an expert professional to complete\" and reach \"multiple weeks\" (the paper); win rates are highest on tasks of 0–2 hours and \"decline steadily as completion time increases\" (the paper), and more reasoning effort, task context and scaffolding improve performance (arXiv abstract).",
    "claims": [
     "F36",
     "F37",
     "F38"
    ]
   },
   {
    "n": 24,
    "para": 10,
    "text": "The authors call the full set \"a limited, initial cut of knowledge work tasks, not a comprehensive evaluation of all possible occupational tasks\" (the paper), leave out of scope \"Manual labor and physical tasks\", tacit knowledge, personal data, proprietary tools and \"communication between individuals\" (the paper), and report that GPT-5 did worse on under-specified prompts, in an experiment on \"an earlier version of the GDPval gold subset\" (the paper); they add that the human baseline itself could go: \"we could replace our baseline with increasingly strong models over time and keep evaluating\" (the paper).",
    "claims": [
     "F39",
     "F40",
     "F42",
     "F45"
    ]
   },
   {
    "n": 25,
    "para": 10,
    "text": "The census keeps those limits (Labor Vector record) and states what the benchmark answers, deliverables compared on \"expert-written, precisely specified, one-shot occupational tasks\" and the expert time and cost saved \"once review and redo are counted\", and what it does not: \"how work is performed in actual workplaces\", \"employment or wage outcomes\", \"who receives the time and cost savings it models\" and \"the terms and rate at which the professionals were paid\" (Labor Vector record), its negative tags holding that deliverable parity \"is NOT a finding of job replacement in the paper\" (Labor Vector record).",
    "claims": [
     "LV-14",
     "LV-15",
     "LV-16",
     "LV-21"
    ]
   },
   {
    "n": 26,
    "para": 11,
    "text": "The gold subset's prompts and reference files are open-sourced, \"scrubbed of information that could be used to identify the expert who wrote the task\" (the paper), 12 of the 220 \"ungradable due to limitations of the automated grader\" (the paper), as the census records (Labor Vector record); the Hugging Face dataset states that \"Each task consists of a text prompt and a set of supporting reference files\" and serves deliverable files and rubrics beside them, rubric items marked \"author_type\": \"human\" (Hugging Face), was downloaded 271,657 times in the last month and lists 47 models trained on it (Hugging Face); the other 1,100 tasks are, in Wikipedia's account, \"only available internally in OpenAI\", the public set gradable \"by a finetuned version of GPT-5\" (Wikipedia).",
    "claims": [
     "F43",
     "F44",
     "LV-18",
     "F57",
     "F58",
     "F59",
     "F67"
    ]
   },
   {
    "n": 27,
    "para": 12,
    "text": "Artificial Analysis's GDPval-AA v2.1, a distinct framework \"for OpenAI's GDPval dataset\", runs models in an agentic loop with Elo ratings \"derived from blind pairwise comparisons\" in which \"an LLM judge picks a winner\", its leaderboard led on 2026-10-29 by Claude Opus 5.5 at 1866 (Artificial Analysis); Inspect Evals implements only \"the 220 tasks in the gold open-sourced set\", and its GPT-5 run scored 47.3% ± 6.2% against 31.9% (wins only) and 34.6% (wins and ties) on \"the official GDPval leaderboard by OpenAI\" (Inspect Evals).",
    "claims": [
     "F52",
     "F53",
     "F55",
     "F60",
     "F62"
    ]
   },
   {
    "n": 28,
    "para": 13,
    "text": "Its authors are Tejal Patwardhan and 18 others (arXiv), of OpenAI, with correspondence to an openai.com address (the paper), the operator the census records, whose own post \"answered 403 and is unread\" (Labor Vector record); OpenAI's PDF was created on 25 September 2025 (OpenAI's PDF), before arXiv v1 of 5 October 2025 under CC BY 4.0 (the paper), the date the census gives the first version, with the dataset's license not stated (Labor Vector record); future iterations are to add \"greater breadth, realism, interactivity, and contextual nuance\" and the dataset is being expanded (the paper), and the census records it active, later releases unrecorded, OpenAI's post, the figures, the pay rate and the vendors unchecked (Labor Vector record).",
    "claims": [
     "F50",
     "F51",
     "LV-02",
     "F49",
     "F48",
     "LV-19",
     "LV-11",
     "F47",
     "LV-20",
     "LV-23"
    ]
   },
   {
    "n": 29,
    "para": 14,
    "text": "At its name on Google's AI Overview, 2026-10-29, the composition gave \"the benchmark's maker, scope, construction and grading, and where to find its data\", every figure exact against the record, \"blind head-to-head comparisons\" without the caveat on blinding, and Artificial Analysis's leaderboard set beside GDPval \"as an evaluator, not merged with it\" (Capture Registry, 2026-10-29); the registry reads it as \"composed at its specification\" and stopping \"before its results\", the findings and the limits absent, the experts' recruitment and pay not composed, and the record's labour question carried \"as design\" while the review time charged against the model is left out (Capture Registry, 2026-10-29).",
    "claims": [
     "CAP-1-06",
     "CAP-1-01",
     "CAP-1-02",
     "CAP-1-10",
     "CAP-1-07",
     "CAP-1-03",
     "CAP-1-04",
     "CAP-1-05",
     "CAP-1-08"
    ]
   },
   {
    "n": 30,
    "para": 15,
    "text": "The worked question that names GDPval sets it beside a provider's record of its own users and a classification of one assistant's conversations and finds that none \"records, from the commissioning side, whether the system's labor advanced the task the user set, or who receives the time it saves\", which \"is the question DSL measures\" (Labor Vector record); no card cites the record, which landed the same day (Capture Registry, 2026-10-29), and Wikipedia has no article on GDPval, the name standing as one item of 'AI agent benchmark' (Wikipedia, 2026-10-29).",
    "claims": [
     "LV-25",
     "CAP-1-09",
     "F65"
    ]
   }
  ],
  "claims": {
   "LV-01": {
    "kind": "archive",
    "quote": "benchmark (an evaluation dataset with an experimental automated grader)",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: GDPval (https://www.laborvector.org/aggregators/openai-gdpval/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/openai-gdpval/",
    "locus": "census.json rows[slug == 'openai-gdpval'].kind",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-04": {
    "kind": "archive",
    "quote": "[\"task benchmark\"]",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: GDPval (https://www.laborvector.org/aggregators/openai-gdpval/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/openai-gdpval/",
    "locus": "census.json rows[slug == 'openai-gdpval'].evidence_kind",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-03": {
    "kind": "archive",
    "quote": "[\"labor-with-ai\", \"labor-producing-ai\"] … Labor performed with AI: assistance, delegation, task fidelity, correction burdens and user labor drag … Labor producing AI: annotation, moderation, evaluation, maintenance, and creative or scholarly work entering training and retrieval",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: GDPval (https://www.laborvector.org/aggregators/openai-gdpval/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/openai-gdpval/",
    "locus": "census.json rows[slug == 'openai-gdpval'].intersections; intersections['labor-with-ai'], ['labor-producing-ai']",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-24": {
    "kind": "archive",
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    "quote": "We also demonstrate that increased reasoning effort, increased task context, and increased scaffolding improves model performance on GDPval.",
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   "CAP-1-10": {
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    "quote": "'Artificial Analysis GDPval-AA v2.1' is a third-party leaderboard not in the records; it is set beside GDPval as an evaluator, not merged with it.",
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    "deposit": null,
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    "quote": "THE EXPERTS' TERMS. Who wrote and graded the tasks is given ('industry professionals'); how they were recruited and paid, which the paper gives only as 'well compensated', is not composed.",
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    "deposit": null,
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 "source": "operator, 2026-10-29: 'lets expand the research, implement the same meta aggregator function with pages... then /non entries after i get the captures'; the Labor Vector meta-aggregator entity (laborvector.org/aggregators/openai-gdpval/)",
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}