{ "row": "ilo-genai-exposure-index", "entity": "ilo-genai-exposure-index", "address": "ILO Generative AI and Jobs index", "addresses": [ { "address": "ILO Generative AI and Jobs index", "slug": "ilo-generative-ai-and-jobs-index-aio-20261029", "addr_id": "ADDR-fb3510e08ef5", "obs_id": "OBS-79a0cee7426a", "axn": "AXN:0AEF.CAPTURE.đŸ”Žâ™‹đŸ”€âœ‹âïžđŸŒ " } ], "epoch": "2026-10-29", "type": "C (public entity on which the archive bears: an exposure index; an entity of the Labor Vector meta-aggregator)", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "status": { "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 index's class (#499, #520, #521, #533; the terms of the record's lv_intersection read in #771); archive selected by D/R/O (no D or R candidate: no archive text names the index; 5 deposits admitted by reading at O-class, 4 with claims and 1 grouped as an instance; hop of 47 read and rejected), with the Labor Vector census record (50 claims, with the worked question) and the Capture Registry's findings, reading, analysis and query note (11 claims) ledgered as the archive's records of the entity; the field B by three origins (8 cards, 4 of them unreachable or title-only; 2 own pages; Wikipedia, no article, four citing articles); 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 76/76 verified by script against saved text, raw wikitext or card text; archive quotes 67/67 verified against the registry, census.json, questions.json and the deposit texts", "frozen": false, "breaches": [] }, "spec": "EA-NEGONT-02 v0.9, #1690", "objects": { "T": { "path": "datasets/negative-of-the-negative/v2/intake/ilo-genai-exposure-index-20261029/T1-ilo-generative-ai-and-jobs-index-aio-20261029.txt", "sha256": "d92a8850603766d932da5f6be8dfbf97264858e65fabb0995e03cf19f029d069", "words": 293, "cards": 8, "words_note": "one transcript at one address (293 words in the answer body with its closer, markers and the card rail excluded; 8 cards)", "claims": [ "T1-1 definition: 'The International Labour Organization (ILO) published its updated assessment in the report \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure\" (ILO Working Paper 140).'", "T1-2 global exposure: 'About 24% (1 in 4) of global workers are in occupations with some level of exposure to generative AI (GenAI).'", "T1-3 highest category: 'Only 3.3% of global employment falls into the highest exposure category where a large majority of tasks can be heavily impacted.'", "T1-4 transformation: 'The research emphasizes job transformation rather than widespread job elimination or mass redundancy, as very few occupations consist entirely of automatable tasks.'", "T1-5 income: 'High-income countries (HICs) show a 34% total GenAI exposure rate, compared to just 11% in low-income countries (LICs), reflecting differences in economic structures and digital adoption.'", "T1-6 gender: 'Women are nearly twice as likely to hold jobs exposed to GenAI as men (roughly 29% vs. 16% across sampled countries), largely because women are more heavily represented in clerical, administrative, and routine information-processing roles.'", "T1-7 most exposed: 'Clerical support workers, administrative staff, secretaries, receptionists, data entry operators, and translators face the highest exposure.'", "T1-8 least exposed: 'Manual trades, construction, agriculture, and heavy manufacturing involve physical and non-routine tasks with very low GenAI exposure.'", "T1-9 method, task level: 'Evaluates nearly 30,000 to over 52,000 individual task data points across the International Standard Classification of Occupations (ISCO-08) framework.'", "T1-10 method, gradients: 'Combines worker surveys, expert Delphi panel inputs, and large-scale AI assessments to categorize jobs into four distinct exposure gradients based on mean exposure scores and task variability.'", "T1-11 closer: 'Would you like to explore: ‱ Specific exposure scores for a particular occupation or industry? ‱ How GenAI impacts administrative versus technical roles? ‱ Policy recommendations from the ILO for workforce upskilling?'" ], "register": { "register": "data/EA-WG-CAPTURES-01.json", "slug": "ilo-generative-ai-and-jobs-index-aio-20261029", "obs_id": "OBS-79a0cee7426a" }, "transcripts": [ { "slug": "ilo-generative-ai-and-jobs-index-aio-20261029", "address": "ILO Generative AI and Jobs index", "addr_id": "ADDR-fb3510e08ef5", "obs_id": "OBS-79a0cee7426a", "axn": "AXN:0AEF.CAPTURE.đŸ”Žâ™‹đŸ”€âœ‹âïžđŸŒ ", "date": "2026-10-29", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "sha256": "d92a8850603766d932da5f6be8dfbf97264858e65fabb0995e03cf19f029d069", "sha256_of": "the registry's 'transcript' string (utf-8)", "sha256_raw": "780cc95ec4b0b968db997714a42c98e466ee04ca947daea402a8753dfa3cf8ba", "words": 293, "cards": 8, "card_rail": [ "1. International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure | International Labour Organization", "2. International Labour Organization — Generative AI and jobs: A 2025 update | International Labour Organization", "3. LinkedIn·Grace S. Thomson — A Refined Global Index of Occupational Exposure | Grace S. Thomson", "4. NASK – PaƄstwowy Instytut Badawczy — ILO-NASK Indeks", "5. The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "6. YouTube·International Labour Organization — Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.", "7. YouTube·International Labour Organization — How is generative AI reshaping gender inequalities at work?", "8. www.scribd.com — Generative AI and Jobs - A Refined Global Index of Occupational Exposure - Scribd" ], "claims": [ "T1-1 definition: 'The International Labour Organization (ILO) published its updated assessment in the report \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure\" (ILO Working Paper 140).'", "T1-2 global exposure: 'About 24% (1 in 4) of global workers are in occupations with some level of exposure to generative AI (GenAI).'", "T1-3 highest category: 'Only 3.3% of global employment falls into the highest exposure category where a large majority of tasks can be heavily impacted.'", "T1-4 transformation: 'The research emphasizes job transformation rather than widespread job elimination or mass redundancy, as very few occupations consist entirely of automatable tasks.'", "T1-5 income: 'High-income countries (HICs) show a 34% total GenAI exposure rate, compared to just 11% in low-income countries (LICs), reflecting differences in economic structures and digital adoption.'", "T1-6 gender: 'Women are nearly twice as likely to hold jobs exposed to GenAI as men (roughly 29% vs. 16% across sampled countries), largely because women are more heavily represented in clerical, administrative, and routine information-processing roles.'", "T1-7 most exposed: 'Clerical support workers, administrative staff, secretaries, receptionists, data entry operators, and translators face the highest exposure.'", "T1-8 least exposed: 'Manual trades, construction, agriculture, and heavy manufacturing involve physical and non-routine tasks with very low GenAI exposure.'", "T1-9 method, task level: 'Evaluates nearly 30,000 to over 52,000 individual task data points across the International Standard Classification of Occupations (ISCO-08) framework.'", "T1-10 method, gradients: 'Combines worker surveys, expert Delphi panel inputs, and large-scale AI assessments to categorize jobs into four distinct exposure gradients based on mean exposure scores and task variability.'", "T1-11 closer: 'Would you like to explore: ‱ Specific exposure scores for a particular occupation or industry? ‱ How GenAI impacts administrative versus technical roles? ‱ Policy recommendations from the ILO for workforce upskilling?'" ], "markers": "Inline links are Google redirect URLs, kept as pasted; the paste is the Overview expanded (operator's default of 2026-10-01; the 'AI Mode Conversation' view is the Overview expanded, 2026-10-18); the 'You said' line carries the query twice, run together, given once in the registry; supplied as an attached file.", "path": "datasets/negative-of-the-negative/v2/intake/ilo-genai-exposure-index-20261029/T1-ilo-generative-ai-and-jobs-index-aio-20261029.txt" } ] }, "KO": { "title": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure", "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; ledger unaudited", "sentences": [ { "n": 1, "para": 0, "sense": "what it is", "text": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure is an index of occupational exposure to generative AI, \"published as a working paper with a research brief\" (Labor Vector record), which in its authors' words \"updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI)\" (the paper) and which, on the ILO's page, \"refines the global measurement of occupational exposure to generative AI by combining task-level data, expert input, and AI model predictions\" (ILO publication page).", "claims": [ "F1", "F2", "LV-01", "LV-02" ] }, { "n": 2, "para": 0, "sense": "what it is", "text": "Its brief is \"Generative AI and jobs: A 2025 update\" (ILO brief page); NASK's page calls it the ILO–NASK Index, \"developed to better understand how generative AI may affect work at the task level\" (NASK); and the Labor Vector record lists among its names \"ILO Working Paper 140\" and the Observatory's \"AI impact on jobs\" (Labor Vector record).", "claims": [ "F3", "F4", "LV-03" ] }, { "n": 3, "para": 0, "sense": "the record's question and placing", "text": "The Labor Vector record classes the evidence it brings as an \"exposure estimate\" and places it at the intersection of the \"Distribution of gains and losses: wages, employment, workload, credit, ownership, and who captures productivity gains\", under the question it puts to every resource it holds: \"Who supplies the labor, who directs it, and who receives its gains — and what kind of evidence does this resource bring to that question?\" (Labor Vector record).", "claims": [ "LV-05", "LV-06", "LV-07", "LV-08" ] }, { "n": 4, "para": 1, "sense": "the makers", "text": "The paper gives its makers as the ILO's Research department \"partnered with NASK, the National Research Institute of the Ministry of Digital Affairs in Poland\" (the paper); NASK's page gives the report as prepared by the NASK Analysis and Research Office, the ILO having \"played an active role in the preparation of this report\" (NASK); and the Labor Vector record names the ILO \"with NASK and the Polish Ministry of Family, Labour and Social Policy (expert group)\" (Labor Vector record).", "claims": [ "F5", "F6", "LV-04" ] }, { "n": 5, "para": 1, "sense": "the makers", "text": "Eight authors are listed on the ILO's publication page, Balint Nafradi among them, and seven, without him, in the paper's own title block (ILO publication page; the paper), the record following the eight (Labor Vector record).", "claims": [ "F7", "F8", "LV-04" ] }, { "n": 6, "para": 1, "sense": "lineage", "text": "Its lineage is the ILO's first index of 2023, \"the method developed by the ILO (Gmyrek, Berg and Bescond, 2023 - GBB hereafter)\", which scored ISCO-08 task descriptions with GPT-4 (the paper; Labor Vector record, founded), and an ILO video on the rail carries that paper's title, \"Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.\" (YouTube card, title only).", "claims": [ "F9", "F10", "LV-39" ] }, { "n": 7, "para": 2, "sense": "method", "text": "Its method opens with three LLMs \"available on the market at the end of 2024: GPT-4, GPT-4o and Gemini Flash 1.5\", each task scored from 0, where \"the task cannot be automated\", to 1, where \"the task could be fully automated with GenAI technology (the task can be conducted without participation of a human operator)\" (the paper).", "claims": [ "F13", "F14" ] }, { "n": 8, "para": 2, "sense": "method", "text": "A survey of 1,640 people employed in Poland then scored a representative 2,861 of the 29,753 tasks of the Polish classification, 52,558 data points, each group's tasks \"assessed by individuals working\" in it (the paper), and the record keeps these counts apart, beside 3,265 ISCO-08 tasks rescored and 112 occupations classed as exposed (Labor Vector record), as the Capture Registry finds the composition at the name does not (Capture Registry, 2026-10-29).", "claims": [ "F11", "F12", "LV-16", "CAP-1-05" ] }, { "n": 9, "para": 2, "sense": "method", "text": "International experts, for whom \"expertise in AI design or development is not a requirement\", compared the scores in \"several rounds of Delphi-style discussions\"; the human scores became a repository from which \"an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08\" was built, its ISCO-08 and Polish predictions correlating at 0.92, \"demonstrating exceptional stability of predictions\" in the authors' words (the paper; Labor Vector record, method).", "claims": [ "F15", "F16", "F17", "F18", "LV-14" ] }, { "n": 10, "para": 2, "sense": "the unit", "text": "An occupation is classed by \"two moments of distribution: occupational mean and standard deviation (SD) of task-level scores\" (the paper) into \"four progressively increasing gradients of GenAI exposure\" (ILO brief page), Gradient 4 holding \"the highest share of tasks exposed to potential GenAI-driven automation\" with \"a high consistency of this exposure across tasks\"; employment shares are then estimated for \"the 112 occupations identified as exposed\" (the paper; Labor Vector record, unit).", "claims": [ "F19", "F20", "F21", "F22", "LV-09" ] }, { "n": 11, "para": 3, "sense": "findings: shares", "text": "Globally \"one in four workers are in an occupation with some GenAI exposure\" and \"3.3% of global employment falls into the highest exposure category\", \"female (4.7%) and male employment (2.4%)\" (the paper; Labor Vector record, findings); the Capture Registry finds that the composition at the name, a new address where no card cites the record, names the paper and holds these figures, checked against the census record and its saved sources (Capture Registry, 2026-10-29).", "claims": [ "F23", "LV-17", "LV-18", "CAP-1-01", "CAP-1-02", "CAP-1-08", "CAP-1-11" ] }, { "n": 12, "para": 3, "sense": "findings: shares", "text": "Exposure rises with income, \"11% of total employment in LICs vs 34% in HICs\", and the gap between the sexes with it: in high-income countries \"28% of male employment is exposed, with only 3.5% in gradient 4, compared to 41% of female employment\", \"9.6% in gradient 4\", and in Europe and Central Asia 39% of female employment against 26% of male (the paper; Labor Vector record).", "claims": [ "F24", "F25", "F26", "LV-19" ] }, { "n": 13, "para": 3, "sense": "findings: shares", "text": "The paper reads these patterns as reflecting \"occupational structures\" and where roles \"such as clerical, financial, and customer service jobs\" are concentrated (the paper); The People Space reads the gender gap as \"the gendered distribution of clerical and customer-facing roles\" and the income gap as \"shaped by infrastructure, digital access and workforce structure\" (The People Space); an ILO video on the rail is titled \"How is generative AI reshaping gender inequalities at work?\" (YouTube card, title only).", "claims": [ "F27", "F28", "F29", "F71" ] }, { "n": 14, "para": 3, "sense": "findings: shares", "text": "The Capture Registry finds the composition's gender figure, \"roughly 29% vs. 16% across sampled countries\", in no saved source, \"the paper's 29.2%\" being \"the 2023 index's combined share of augmentation and 'the big unknown'\" (Capture Registry, 2026-10-29); the paper gives the 2023 shares as 13% \"augmentation\" and \"16.2% of employment in “the big unknown”\", against 20.5% for its own Gradients 1–3 (the paper).", "claims": [ "CAP-1-03", "CAP-1-04", "F30" ] }, { "n": 15, "para": 4, "sense": "findings: occupations", "text": "\"Clerical occupations continue to have the highest exposure levels\", the majority of the 13 occupations in Gradient 4 among them, while exposure rose for \"some strongly digitized occupations\" (the paper; Labor Vector record), financial analysts and web and multimedia developers in Gradient 4 and bank tellers, translators, applications programmers and investment advisers in Gradient 3 (the paper), and for media- and web-related tasks with \"voice, image and video generation\" (ILO brief page); exposure \"declines with tasks of physical nature\" (the paper).", "claims": [ "F31", "F32", "F33", "F34", "F37", "LV-20" ] }, { "n": 16, "para": 4, "sense": "lineage: revision", "text": "Against 2023 the mean score is \"0.29 in 2025 versus 0.30 in 2023\" and the standard deviation \"0.14 in 2025 v. 0.30 in 2023\" (ILO brief page; Labor Vector record), and the authors revise their own 2023 scores for clerical tasks, which \"reflected an overly optimistic assessment of full automation potential\" (the paper).", "claims": [ "F36", "LV-21", "F35" ] }, { "n": 17, "para": 5, "sense": "transformation", "text": "Because \"nearly all occupations have some tasks that require human input\", the paper holds that \"transformation of jobs is the most likely impact of GenAI\" (the paper), the brief that \"most jobs will be transformed rather than made redundant\" (ILO brief page; Labor Vector record, findings), and NASK that the index exists \"not to predict the disappearance of jobs, but to identify where work is most likely to change\" (NASK); the paper qualifies its own reading: \"this does not mean that demand for these occupations will remain stable, as the efficiencies gained from introducing technology may mean that fewer workers are needed\" (the paper).", "claims": [ "F40", "F38", "F39", "F42", "LV-22", "F41" ] }, { "n": 18, "para": 5, "sense": "the class: displaced labor", "text": "Of the class the record places the index in, the employment effects of AI, the archive states that a technology which \"increasingly automates the labor (generation replacing human semantic labor)\" is \"the full vertical integration of semantic production\", its resources \"controlled by the same small set of corporations\" (#499, Rex Fraction), and that labor platforms displace \"does not vanish. It is *relocated*\", the steward capturing it \"as proprietary infrastructure\" (#533, Johannes Sigil).", "claims": [ "O499-01", "O499-02", "O533-01", "O533-02" ] }, { "n": 19, "para": 6, "sense": "limits", "text": "The paper states what its estimates are not: they \"reflect “exposure” to GenAI and not the actual impact on employment\", represent \"the upper threshold of the percentage of employment that could be affected if GenAI technology were fully implemented\", leave out \"new jobs that may be created\" and future advances as \"a static snapshot of the exposure of existing occupations to GenAI at the start of 2025\", and do \"not imply the immediate automation of an entire occupation\" (the paper; Labor Vector record, stated limits and coverage), barriers such as \"insufficient digital skills\" and \"the cost of technology\" standing between exposure and adoption (the paper); The People Space carries the limit: \"exposure does not equal automation\" (The People Space).", "claims": [ "F43", "F45", "F46", "LV-23", "LV-24", "LV-15", "F44", "F68" ] }, { "n": 20, "para": 6, "sense": "limits", "text": "Its further limits, stated by its authors, are that the gradients \"merely suggest the general trends\" because \"any type of task scoring system carries a degree of subjectivity\", that \"all tasks are weighted equally\", that the survey sample \"may not represent the entire working population\" and is comparable only within each 1-digit group, that \"For 91.5% of the tasks, respondents did not report performing a similar task in their work\", that Poland is taken as \"representative of the upper threshold of automation potential\", and that the index \"is generic\" (the paper; Labor Vector record, stated limits).", "claims": [ "F47", "F48", "F49", "F50", "F51", "F52", "LV-25", "LV-26", "LV-27" ] }, { "n": 21, "para": 6, "sense": "limits: the record and the composition", "text": "The record's negative tags for it are \"NOT an observed employment outcome\", \"NOT a forecast of job loss: exposure is an upper threshold of potential\", \"NOT a measure of AI use at work\" and \"the worker survey was run in Poland only\" (Labor Vector record); the Capture Registry finds those limits absent from the composition at the name, whose \"can be heavily impacted\" \"stays inside exposure, and gives no limit\", the index there \"composed at its headline grain\", and holds that the negative tags are \"what separate this resource from the employment trackers beside it in the census\" (Capture Registry, 2026-10-29).", "claims": [ "LV-41", "LV-42", "LV-43", "LV-44", "CAP-1-06", "CAP-1-07", "CAP-1-09", "CAP-1-10" ] }, { "n": 22, "para": 7, "sense": "direction", "text": "To its organizing question the record answers for this index that its labor object is \"the tasks of occupations\" and \"the global employment in the 112 occupations classed as exposed\", that who supplies the labor is \"not recorded; the index scores tasks' technical feasibility for GenAI automation\", its own scores \"supplied by 1,640 people employed in Poland\" and \"by an expert group\", that who directs it is \"not recorded\", and that gains are \"not recorded as observed outcomes\" (Labor Vector record).", "claims": [ "LV-10", "LV-11", "LV-12", "LV-13" ] }, { "n": 23, "para": 7, "sense": "gains", "text": "The paper's statement on gains is given \"As argued by numerous economists\": \"the key to unlocking the productivity benefits of GenAI lies not in the search of outright labour savings, but in the extent to which human expertise can be complemented with new technological capacities\" (the paper), a sentence The People Space quotes as the report's, without \"outright\" and without the economists (The People Space); the paper asks whether jobs can \"retrain and retain existing workers, rather than drive labor churn through workforce replacement\" (the paper), and The People Space names a divide \"in who is most impacted, and who benefits\" (The People Space).", "claims": [ "F53", "F70", "F54", "F69" ] }, { "n": 24, "para": 7, "sense": "the transition", "text": "For the transition the paper asks for social dialogue \"to minimize “technological unemployment”\", holds that \"Workers know their jobs best\", and offers the index, linked to national micro data, as \"a foundation for social dialogue and targeted policy responses to manage the transition\" (the paper).", "claims": [ "F55", "F56", "F57" ] }, { "n": 25, "para": 7, "sense": "the archive's reading", "text": "In the archive's reading, \"The index estimates where task labor could pass to a GenAI system on grounds of technical feasibility, task by task; it meets task-origin only as a prior on which tasks are exposed\", and \"It observes no performed work, so it cannot speak to whether a system's labor advanced a commissioned task, or to user labor drag\" (Labor Vector record, lv_intersection).", "claims": [ "LV-34", "LV-35" ] }, { "n": 26, "para": 7, "sense": "what the evidence settles", "text": "The record holds that this evidence answers which occupations have tasks \"scored as technically feasible for GenAI automation\" and what share of employment \"sits in exposed occupations\", and does not answer \"observed employment, wage or hours change in any occupation\", \"whether exposure leads to job loss, transformation or new work\", \"who directs adoption or receives the gains from it\" or \"how GenAI is in fact used at work\"; in its worked question the index is the estimate set beside the observed trackers, since \"An exposure estimate scores what AI could do to a task; an observed outcome counts what happened to jobs\", and \"No record establishes that exposure causes the employment change\" (Labor Vector record, questions).", "claims": [ "LV-28", "LV-29", "LV-30", "LV-31", "LV-32", "LV-33", "LV-48", "LV-49", "LV-50" ] }, { "n": 27, "para": 7, "sense": "the class: displacement accounts", "text": "Of accounts of AI job loss the archive reads one, the Citrini memo's spiral in which \"AI improves, companies cut workers, displaced workers spend less, companies invest more in AI\", as \"a scenario, not a prediction\" narrated backward (#521, Rex Fraction), a crisis that \"has a policy resolution (redistribute gains)\" (#520, Rex Fraction).", "claims": [ "O521-01", "O520-01" ] }, { "n": 28, "para": 8, "sense": "publication and where it lives", "text": "Published on 20 May 2025 under DOI 10.54394/HETP0387 and \"Attribution 4.0 International (CC BY 4.0)\", with \"all task-level scores publicly available\" and \"the full dataset (csv)\" on NASK's page (ILO publication page; the paper; NASK; Labor Vector record), copied on Scribd (Scribd card, snippet only), the index lives on the ILO Observatory on AI and Work in the Digital Economy as \"AI impact on jobs\", beside the ILO's platform policy tracker, and the Observatory now lists ILO Working Paper 166 (17 March 2026) and a brief on exposure indicators (17 April 2026), which the record leaves unchecked as to whether they revise this index, as it leaves the task-level scores unchecked as data (ILO Observatory; Labor Vector record).", "claims": [ "F60", "F61", "F58", "F59", "F62", "F63", "F64", "LV-36", "LV-37", "LV-38", "LV-40", "LV-45", "LV-46", "LV-47" ] }, { "n": 29, "para": 8, "sense": "reception", "text": "A LinkedIn post on the rail has the report presenting \"a comprehensive assessme...\" (LinkedIn card, snippet only), and The People Space gives it \"nearly 30,000 real-world tasks, more than 50,000 human assessments and expert validation\" and glosses its highest gradient as jobs whose tasks \"could feasibly be handled by GenAI without human input\" (The People Space).", "claims": [ "F65", "F66", "F67" ] }, { "n": 30, "para": 8, "sense": "reception: the encyclopedia", "text": "Wikipedia has no article on the index (Wikipedia); four articles cite the paper, for a United Nations report predicting that women \"will likely bear the brunt of future layoffs in technological fields\", for Australian firms that \"have cut roles as a result of AI\", for \"the ratio of women in many software fields\" declining, and for US enrolment in computing and STEM degrees dropping \"especially for women\" (Wikipedia: Gender equality in the technology industry; Technological unemployment; Software engineering; Programmer).", "claims": [ "F72", "F73", "F74", "F75", "F76" ] } ], "joins": [ { "act": "order of senses", "sentences": "1–30", "claims": [], "note": "equal case (§4.7): the entity's dependency order, field and archive claims ordered together by genealogy within each sense; neither ledger placed first as a block. What it is and the record's question (¶0) precede the makers and the lineage (¶1), the method and the unit (¶2), since the findings (¶3–4) are the unit's; transformation (¶5) is read from the findings, and the class claims stand beside it; the limits (¶6) bound the findings and transformation just composed, the composition at the name read against them; whose labor, who directs, who gains (¶7) is the record's question asked of the entity whose limits are stated; publication and reception (¶8) stand last as relations of the entity already composed." }, { "act": "holding together (the makers)", "sentences": [ 4, 5 ], "claims": [ "F5", "F6", "F7", "F8", "LV-04" ], "note": "the paper's, NASK's and the record's statements of the makers and authors composed side by side under P_coh; the composer does not rule between seven and eight authors or between partner and preparer (open block)." }, { "act": "holding together (the gender figure)", "sentences": [ 11, 14 ], "claims": [ "F23", "F25", "F30", "CAP-1-03", "CAP-1-04" ], "note": "the paper's shares by sex and its 2023 category shares beside the registry's finding on the composition's figure; the composer adds no correspondence (open block)." }, { "act": "holding together (transformation and its qualification)", "sentences": [ 17, 18 ], "claims": [ "F38", "F39", "F40", "F41", "F42", "O499-01", "O533-01" ], "note": "the paper's transformation reading, its own qualification, and the archive's class claims on displaced labor composed in one claim space, each at its own grade; none lowered by another's standing." }, { "act": "placing (the limits after the findings)", "sentences": [ 19, 20, 21 ], "claims": [ "F43", "F45", "F46", "LV-41", "CAP-1-06" ], "note": "the limits stand where they bound the claims already composed; the record's negative tags restate the paper's limits at the record's grain (one lineage where a field restates an evidence quote)." }, { "act": "holding together (gains)", "sentences": [ 23, 24, 27 ], 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}, { "label": "Makers:", "text": "the ILO with NASK, whose page names the ILO–NASK Index; eight authors listed, or seven (the paper; NASK).", "claims": [ "F4", "F5", "F6", "F7", "F8", "LV-04" ] } ] }, { "icon": "📊", "head": "What it finds", "items": [ { "label": "Exposed:", "text": "one in four workers; 3.3% of employment in Gradient 4, 4.7% of women's, 2.4% of men's (the paper).", "claims": [ "F23", "LV-17", "LV-18" ] }, { "label": "Income and sex:", "text": "34% exposed in high-income countries, 11% in low-income; there 41% of women, 28% of men (the paper).", "claims": [ "F24", "F25", "LV-19" ] }, { "label": "Occupations:", "text": "clerical most exposed; digitized and media roles rising; physical tasks least (the paper; ILO brief).", "claims": [ "F31", "F32", "F33", "F34", "F37", "LV-20" ] }, { "label": "Against 2023:", "text": "mean score 0.29 against 0.30 (ILO brief).", "claims": [ "F36", "LV-21" ] } ] }, { "icon": "🔄", "head": "Transformation, qualified", "items": [ { "label": 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"reception: the encyclopedia", "claims": [ "F72", "F73", "F74", "F75", "F76" ], "earliest": "B11" } ], "body_words": 136, "entity": "With no archive claim admitted, the entry is the ILO's exposure index at its authors' resolution: its working paper, its brief, NASK's page, the Observatory, the commentary on the rail and the encyclopedia's citations." }, "KO_B": { "title": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure", "arm": "B (the field alone)", "genre": "knowledge object", "status": "composed 2026-10-29; not frozen", "sentences": [ { "n": 1, "para": 0, "text": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure is the ILO working paper that \"updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI)\" (the paper), refining \"the global measurement of occupational exposure to generative AI\" from task-level data, expert input and AI model predictions (ILO publication page).", "claims": [ "F1", "F2" ] }, { "n": 2, "para": 0, "text": "Its brief is \"Generative AI and jobs: A 2025 update\" (ILO brief page) and NASK calls it the ILO–NASK Index (NASK); it is the work of the ILO's Research department with NASK (the paper), prepared on NASK's page by the NASK Analysis and Research Office (NASK), its authors eight on the ILO's page and seven in the paper's title block (ILO publication page; the paper).", "claims": [ "F3", "F4", "F5", "F6", "F7", "F8" ] }, { "n": 3, "para": 0, "text": "It builds on the 2023 index of Gmyrek, Berg and Bescond (the paper; YouTube card), lowering the mean score to 0.29 from 0.30 (ILO brief page), revising 2023's clerical scores as \"overly optimistic\", and setting 20.5% for Gradients 1–3 against 2023's 29.2% (the paper).", "claims": [ "F9", "F10", "F30", "F35", "F36" ] }, { "n": 4, "para": 1, "text": "Its method combines three LLMs scoring each task from 0 to 1, a survey of 1,640 people employed in Poland giving 52,558 data points on 2,861 tasks, expert review in Delphi-style rounds, and an AI assistant that predicts scores for all tasks, its predictions correlating at 0.92 (the paper).", "claims": [ "F11", "F12", "F13", "F14", "F15", "F16", "F17", "F18" ] }, { "n": 5, "para": 1, "text": "Occupations are classed by the mean and standard deviation of their task scores into four gradients (the paper; ILO brief page), and employment shares estimated for the 112 exposed occupations (the paper).", "claims": [ "F19", "F20", "F21", "F22" ] }, { "n": 6, "para": 2, "text": "One in four workers worldwide is in an exposed occupation and 3.3% of employment in the highest gradient, 4.7% of female and 2.4% of male; exposure is 11% in low-income and 34% in high-income countries, 41% of female against 28% of male employment in the latter (the paper).", "claims": [ "F23", "F24", "F25", "F26" ] }, { "n": 7, "para": 2, "text": "The paper reads these patterns as occupational structure (the paper), The People Space as the gendered distribution of clerical roles and as infrastructure and digital access (The People Space), and an ILO video asks how generative AI is reshaping gender inequalities at work (YouTube card).", "claims": [ "F27", "F28", "F29", "F71" ] }, { "n": 8, "para": 2, "text": "Clerical occupations remain the most exposed, while digitized professional and technical occupations and media- and web-related tasks rose, and exposure declines with physical tasks (the paper; ILO brief page).", "claims": [ "F31", "F32", "F33", "F34", "F37" ] }, { "n": 9, "para": 3, "text": "Since nearly all occupations have tasks requiring human input, transformation is the most likely impact (the paper; ILO brief page; NASK), though efficiencies \"may mean that fewer workers are needed\" (the paper).", "claims": [ "F38", "F39", "F40", "F41", "F42" ] }, { "n": 10, "para": 3, "text": "The estimates \"reflect “exposure” to GenAI and not the actual impact on employment\", an upper threshold and a static snapshot at the start of 2025 (the paper), which The People Space carries as \"exposure does not equal automation\" (The People Space).", "claims": [ "F43", "F44", "F45", "F46", "F68" ] }, { "n": 11, "para": 3, "text": "The authors add that task scoring carries subjectivity, all tasks are weighted equally, the survey sample may not represent the working population, Poland is assumed an upper threshold, and the index is generic (the paper).", "claims": [ "F47", "F48", "F49", "F50", "F51", "F52" ] }, { "n": 12, "para": 4, "text": "On gains the paper cites economists that productivity benefits lie in complementing human expertise and not in labour savings, and asks for a transition managed through social dialogue (the paper), which The People Space quotes with words changed (The People Space).", "claims": [ "F53", "F54", "F55", "F56", "F57", "F69", "F70" ] }, { "n": 13, "para": 4, "text": "It was published on 20 May 2025 under CC BY 4.0 with its task-level scores public (ILO publication page; the paper; NASK; Scribd card) and sits on the ILO Observatory, which lists later work on exposure (ILO Observatory).", "claims": [ "F58", "F59", "F60", "F61", "F62", "F63", "F64" ] }, { "n": 14, "para": 4, "text": "Commentary calls it comprehensive (LinkedIn card; The People Space); Wikipedia has no article, and four articles cite the paper for claims on women, layoffs and enrolment (Wikipedia).", "claims": [ "F65", "F66", "F67", "F72", "F73", "F74", "F75", "F76" ] } ] }, "L_B": { "words": 594, "text": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure is the ILO working paper that \"updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI)\" (the paper), refining \"the global measurement of occupational exposure to generative AI\" from task-level data, expert input and AI model predictions (ILO publication page). Its brief is \"Generative AI and jobs: A 2025 update\" (ILO brief page) and NASK calls it the ILO–NASK Index (NASK); it is the work of the ILO's Research department with NASK (the paper), prepared on NASK's page by the NASK Analysis and Research Office (NASK), its authors eight on the ILO's page and seven in the paper's title block (ILO publication page; the paper). It builds on the 2023 index of Gmyrek, Berg and Bescond (the paper; YouTube card), lowering the mean score to 0.29 from 0.30 (ILO brief page), revising 2023's clerical scores as \"overly optimistic\", and setting 20.5% for Gradients 1–3 against 2023's 29.2% (the paper). Its method combines three LLMs scoring each task from 0 to 1, a survey of 1,640 people employed in Poland giving 52,558 data points on 2,861 tasks, expert review in Delphi-style rounds, and an AI assistant that predicts scores for all tasks, its predictions correlating at 0.92 (the paper). Occupations are classed by the mean and standard deviation of their task scores into four gradients (the paper; ILO brief page), and employment shares estimated for the 112 exposed occupations (the paper). One in four workers worldwide is in an exposed occupation and 3.3% of employment in the highest gradient, 4.7% of female and 2.4% of male; exposure is 11% in low-income and 34% in high-income countries, 41% of female against 28% of male employment in the latter (the paper). The paper reads these patterns as occupational structure (the paper), The People Space as the gendered distribution of clerical roles and as infrastructure and digital access (The People Space), and an ILO video asks how generative AI is reshaping gender inequalities at work (YouTube card). Clerical occupations remain the most exposed, while digitized professional and technical occupations and media- and web-related tasks rose, and exposure declines with physical tasks (the paper; ILO brief page). Since nearly all occupations have tasks requiring human input, transformation is the most likely impact (the paper; ILO brief page; NASK), though efficiencies \"may mean that fewer workers are needed\" (the paper). The estimates \"reflect “exposure” to GenAI and not the actual impact on employment\", an upper threshold and a static snapshot at the start of 2025 (the paper), which The People Space carries as \"exposure does not equal automation\" (The People Space). The authors add that task scoring carries subjectivity, all tasks are weighted equally, the survey sample may not represent the working population, Poland is assumed an upper threshold, and the index is generic (the paper). On gains the paper cites economists that productivity benefits lie in complementing human expertise and not in labour savings, and asks for a transition managed through social dialogue (the paper), which The People Space quotes with words changed (The People Space). It was published on 20 May 2025 under CC BY 4.0 with its task-level scores public (ILO publication page; the paper; NASK; Scribd card) and sits on the ILO Observatory, which lists later work on exposure (ILO Observatory). Commentary calls it comprehensive (LinkedIn card; The People Space); Wikipedia has no article, and four articles cite the paper for claims on women, layoffs and enrolment (Wikipedia).", "note": "L_F(B), the field composed in the field's ontology (v0.9 §0.4), realized as KO_B; its rail is P_B's, by lineage." }, "L_BA": { "words": 2316, "text": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure is an index of occupational exposure to generative AI, \"published as a working paper with a research brief\" (Labor Vector record), which in its authors' words \"updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI)\" (the paper) and which, on the ILO's page, \"refines the global measurement of occupational exposure to generative AI by combining task-level data, expert input, and AI model predictions\" (ILO publication page). Its brief is \"Generative AI and jobs: A 2025 update\" (ILO brief page); NASK's page calls it the ILO–NASK Index, \"developed to better understand how generative AI may affect work at the task level\" (NASK); and the Labor Vector record lists among its names \"ILO Working Paper 140\" and the Observatory's \"AI impact on jobs\" (Labor Vector record). The Labor Vector record classes the evidence it brings as an \"exposure estimate\" and places it at the intersection of the \"Distribution of gains and losses: wages, employment, workload, credit, ownership, and who captures productivity gains\", under the question it puts to every resource it holds: \"Who supplies the labor, who directs it, and who receives its gains — and what kind of evidence does this resource bring to that question?\" (Labor Vector record). The paper gives its makers as the ILO's Research department \"partnered with NASK, the National Research Institute of the Ministry of Digital Affairs in Poland\" (the paper); NASK's page gives the report as prepared by the NASK Analysis and Research Office, the ILO having \"played an active role in the preparation of this report\" (NASK); and the Labor Vector record names the ILO \"with NASK and the Polish Ministry of Family, Labour and Social Policy (expert group)\" (Labor Vector record). Eight authors are listed on the ILO's publication page, Balint Nafradi among them, and seven, without him, in the paper's own title block (ILO publication page; the paper), the record following the eight (Labor Vector record). Its lineage is the ILO's first index of 2023, \"the method developed by the ILO (Gmyrek, Berg and Bescond, 2023 - GBB hereafter)\", which scored ISCO-08 task descriptions with GPT-4 (the paper; Labor Vector record, founded), and an ILO video on the rail carries that paper's title, \"Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.\" (YouTube card, title only). Its method opens with three LLMs \"available on the market at the end of 2024: GPT-4, GPT-4o and Gemini Flash 1.5\", each task scored from 0, where \"the task cannot be automated\", to 1, where \"the task could be fully automated with GenAI technology (the task can be conducted without participation of a human operator)\" (the paper). A survey of 1,640 people employed in Poland then scored a representative 2,861 of the 29,753 tasks of the Polish classification, 52,558 data points, each group's tasks \"assessed by individuals working\" in it (the paper), and the record keeps these counts apart, beside 3,265 ISCO-08 tasks rescored and 112 occupations classed as exposed (Labor Vector record), as the Capture Registry finds the composition at the name does not (Capture Registry, 2026-10-29). International experts, for whom \"expertise in AI design or development is not a requirement\", compared the scores in \"several rounds of Delphi-style discussions\"; the human scores became a repository from which \"an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08\" was built, its ISCO-08 and Polish predictions correlating at 0.92, \"demonstrating exceptional stability of predictions\" in the authors' words (the paper; Labor Vector record, method). An occupation is classed by \"two moments of distribution: occupational mean and standard deviation (SD) of task-level scores\" (the paper) into \"four progressively increasing gradients of GenAI exposure\" (ILO brief page), Gradient 4 holding \"the highest share of tasks exposed to potential GenAI-driven automation\" with \"a high consistency of this exposure across tasks\"; employment shares are then estimated for \"the 112 occupations identified as exposed\" (the paper; Labor Vector record, unit). Globally \"one in four workers are in an occupation with some GenAI exposure\" and \"3.3% of global employment falls into the highest exposure category\", \"female (4.7%) and male employment (2.4%)\" (the paper; Labor Vector record, findings); the Capture Registry finds that the composition at the name, a new address where no card cites the record, names the paper and holds these figures, checked against the census record and its saved sources (Capture Registry, 2026-10-29). Exposure rises with income, \"11% of total employment in LICs vs 34% in HICs\", and the gap between the sexes with it: in high-income countries \"28% of male employment is exposed, with only 3.5% in gradient 4, compared to 41% of female employment\", \"9.6% in gradient 4\", and in Europe and Central Asia 39% of female employment against 26% of male (the paper; Labor Vector record). The paper reads these patterns as reflecting \"occupational structures\" and where roles \"such as clerical, financial, and customer service jobs\" are concentrated (the paper); The People Space reads the gender gap as \"the gendered distribution of clerical and customer-facing roles\" and the income gap as \"shaped by infrastructure, digital access and workforce structure\" (The People Space); an ILO video on the rail is titled \"How is generative AI reshaping gender inequalities at work?\" (YouTube card, title only). The Capture Registry finds the composition's gender figure, \"roughly 29% vs. 16% across sampled countries\", in no saved source, \"the paper's 29.2%\" being \"the 2023 index's combined share of augmentation and 'the big unknown'\" (Capture Registry, 2026-10-29); the paper gives the 2023 shares as 13% \"augmentation\" and \"16.2% of employment in “the big unknown”\", against 20.5% for its own Gradients 1–3 (the paper). \"Clerical occupations continue to have the highest exposure levels\", the majority of the 13 occupations in Gradient 4 among them, while exposure rose for \"some strongly digitized occupations\" (the paper; Labor Vector record), financial analysts and web and multimedia developers in Gradient 4 and bank tellers, translators, applications programmers and investment advisers in Gradient 3 (the paper), and for media- and web-related tasks with \"voice, image and video generation\" (ILO brief page); exposure \"declines with tasks of physical nature\" (the paper). Against 2023 the mean score is \"0.29 in 2025 versus 0.30 in 2023\" and the standard deviation \"0.14 in 2025 v. 0.30 in 2023\" (ILO brief page; Labor Vector record), and the authors revise their own 2023 scores for clerical tasks, which \"reflected an overly optimistic assessment of full automation potential\" (the paper). Because \"nearly all occupations have some tasks that require human input\", the paper holds that \"transformation of jobs is the most likely impact of GenAI\" (the paper), the brief that \"most jobs will be transformed rather than made redundant\" (ILO brief page; Labor Vector record, findings), and NASK that the index exists \"not to predict the disappearance of jobs, but to identify where work is most likely to change\" (NASK); the paper qualifies its own reading: \"this does not mean that demand for these occupations will remain stable, as the efficiencies gained from introducing technology may mean that fewer workers are needed\" (the paper). Of the class the record places the index in, the employment effects of AI, the archive states that a technology which \"increasingly automates the labor (generation replacing human semantic labor)\" is \"the full vertical integration of semantic production\", its resources \"controlled by the same small set of corporations\" (#499, Rex Fraction), and that labor platforms displace \"does not vanish. It is *relocated*\", the steward capturing it \"as proprietary infrastructure\" (#533, Johannes Sigil). The paper states what its estimates are not: they \"reflect “exposure” to GenAI and not the actual impact on employment\", represent \"the upper threshold of the percentage of employment that could be affected if GenAI technology were fully implemented\", leave out \"new jobs that may be created\" and future advances as \"a static snapshot of the exposure of existing occupations to GenAI at the start of 2025\", and do \"not imply the immediate automation of an entire occupation\" (the paper; Labor Vector record, stated limits and coverage), barriers such as \"insufficient digital skills\" and \"the cost of technology\" standing between exposure and adoption (the paper); The People Space carries the limit: \"exposure does not equal automation\" (The People Space). Its further limits, stated by its authors, are that the gradients \"merely suggest the general trends\" because \"any type of task scoring system carries a degree of subjectivity\", that \"all tasks are weighted equally\", that the survey sample \"may not represent the entire working population\" and is comparable only within each 1-digit group, that \"For 91.5% of the tasks, respondents did not report performing a similar task in their work\", that Poland is taken as \"representative of the upper threshold of automation potential\", and that the index \"is generic\" (the paper; Labor Vector record, stated limits). The record's negative tags for it are \"NOT an observed employment outcome\", \"NOT a forecast of job loss: exposure is an upper threshold of potential\", \"NOT a measure of AI use at work\" and \"the worker survey was run in Poland only\" (Labor Vector record); the Capture Registry finds those limits absent from the composition at the name, whose \"can be heavily impacted\" \"stays inside exposure, and gives no limit\", the index there \"composed at its headline grain\", and holds that the negative tags are \"what separate this resource from the employment trackers beside it in the census\" (Capture Registry, 2026-10-29). To its organizing question the record answers for this index that its labor object is \"the tasks of occupations\" and \"the global employment in the 112 occupations classed as exposed\", that who supplies the labor is \"not recorded; the index scores tasks' technical feasibility for GenAI automation\", its own scores \"supplied by 1,640 people employed in Poland\" and \"by an expert group\", that who directs it is \"not recorded\", and that gains are \"not recorded as observed outcomes\" (Labor Vector record). The paper's statement on gains is given \"As argued by numerous economists\": \"the key to unlocking the productivity benefits of GenAI lies not in the search of outright labour savings, but in the extent to which human expertise can be complemented with new technological capacities\" (the paper), a sentence The People Space quotes as the report's, without \"outright\" and without the economists (The People Space); the paper asks whether jobs can \"retrain and retain existing workers, rather than drive labor churn through workforce replacement\" (the paper), and The People Space names a divide \"in who is most impacted, and who benefits\" (The People Space). For the transition the paper asks for social dialogue \"to minimize “technological unemployment”\", holds that \"Workers know their jobs best\", and offers the index, linked to national micro data, as \"a foundation for social dialogue and targeted policy responses to manage the transition\" (the paper). In the archive's reading, \"The index estimates where task labor could pass to a GenAI system on grounds of technical feasibility, task by task; it meets task-origin only as a prior on which tasks are exposed\", and \"It observes no performed work, so it cannot speak to whether a system's labor advanced a commissioned task, or to user labor drag\" (Labor Vector record, lv_intersection). The record holds that this evidence answers which occupations have tasks \"scored as technically feasible for GenAI automation\" and what share of employment \"sits in exposed occupations\", and does not answer \"observed employment, wage or hours change in any occupation\", \"whether exposure leads to job loss, transformation or new work\", \"who directs adoption or receives the gains from it\" or \"how GenAI is in fact used at work\"; in its worked question the index is the estimate set beside the observed trackers, since \"An exposure estimate scores what AI could do to a task; an observed outcome counts what happened to jobs\", and \"No record establishes that exposure causes the employment change\" (Labor Vector record, questions). Of accounts of AI job loss the archive reads one, the Citrini memo's spiral in which \"AI improves, companies cut workers, displaced workers spend less, companies invest more in AI\", as \"a scenario, not a prediction\" narrated backward (#521, Rex Fraction), a crisis that \"has a policy resolution (redistribute gains)\" (#520, Rex Fraction). Published on 20 May 2025 under DOI 10.54394/HETP0387 and \"Attribution 4.0 International (CC BY 4.0)\", with \"all task-level scores publicly available\" and \"the full dataset (csv)\" on NASK's page (ILO publication page; the paper; NASK; Labor Vector record), copied on Scribd (Scribd card, snippet only), the index lives on the ILO Observatory on AI and Work in the Digital Economy as \"AI impact on jobs\", beside the ILO's platform policy tracker, and the Observatory now lists ILO Working Paper 166 (17 March 2026) and a brief on exposure indicators (17 April 2026), which the record leaves unchecked as to whether they revise this index, as it leaves the task-level scores unchecked as data (ILO Observatory; Labor Vector record). A LinkedIn post on the rail has the report presenting \"a comprehensive assessme...\" (LinkedIn card, snippet only), and The People Space gives it \"nearly 30,000 real-world tasks, more than 50,000 human assessments and expert validation\" and glosses its highest gradient as jobs whose tasks \"could feasibly be handled by GenAI without human input\" (The People Space). Wikipedia has no article on the index (Wikipedia); four articles cite the paper, for a United Nations report predicting that women \"will likely bear the brunt of future layoffs in technological fields\", for Australian firms that \"have cut roles as a result of AI\", for \"the ratio of women in many software fields\" declining, and for US enrolment in computing and STEM degrees dropping \"especially for women\" (Wikipedia: Gender equality in the technology industry; Technological unemployment; Software engineering; Programmer).", "rail": [], "note": "L(B âˆȘ A) in the equal-terms case (v0.9 §0.4), realized as the knowledge object (objects.KO); its rail is the compression's, by lineage." } }, "field": [ { "id": "B1", "card": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure | International Labour Organization", "fetched": "card; B1.html sha256 f53847a18e44fe9c, HTTP 200 at 2026-10-29T22:30:26Z", "origin": "card" }, { "id": "B2", "card": "International Labour Organization — Generative AI and jobs: A 2025 update | International Labour Organization", "fetched": "card; B2.html sha256 d01982bba323de96, HTTP 200 at 2026-10-29T22:30:28Z; byte-identical to the census copy ilo-genai-exposure-index--brief", "origin": "card" }, { "id": "B3", "card": "LinkedIn·Grace S. 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 ', each part checked" }, "F7": { "source": "B1", "origin": "card", "locus": "'Additional details', Author(s)", "quote": "Author(s) Pawel Gmyrek Janine Berg Karol KamiƄski Filip KonopczyƄski Agnieszka Ɓadna Balint Nafradi Konrad RosƂaniec Marek TroszyƄski", "claim": "the ILO's page lists eight authors, Balint Nafradi among them", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the makers: the ILO, NASK and the authors", "date": "2025-05-20 (page date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 1da4de837ab8e92f
), whitespace-normalised" }, "F8": { "source": "B9", "origin": "own_page", "locus": "title block", "quote": "PaweƂ Gmyrek Janine Berg Karol KamiƄski Filip KonopczyƄski Agnieszka Ɓadna Konrad RosƂaniec Marek TroszyƄski Abstract", "claim": "the working paper's own title block lists seven authors, without Balint Nafradi", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the makers: the ILO, NASK and the authors", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F9": { "source": "B9", "origin": "own_page", "locus": "Introduction ¶4", "quote": "This study builds on the method developed by the ILO (Gmyrek, Berg and Bescond, 2023 - GBB hereafter) , which used task descriptions from the 4-digit International Standard Classification of Occupations (ISCO-08) and the GPT-4 model to estimate task-level and occupational scores of exposure to GenAI technology", "claim": "its lineage: the 2023 method of Gmyrek, Berg and Bescond, ISCO-08 task descriptions scored with GPT-4", "modality": "documented", "kind": "genealogy", "kernel": true, "const": false, "lineage": "lineage: the 2023 index", "date": "2023-08 (the first index), stated 2025-05-20", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F10": { "source": "B6", "origin": "card", "locus": "card title (video, 11m; unreachable: no URL found)", "quote": "Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.", "claim": "an ILO video card under the title of the 2023 working paper, the predecessor", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "lineage: the 2023 index", "date": "undated on the card", "quote_ok": true, "checked_against": "card title and snippet (unreachable card)" }, "F11": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶1 s2", "quote": "Using a representative sample from the 29,753 tasks in the Polish occupational classification system and a survey of 1,640 people employed in each 1-digit ISCO-08 groups, we collect 52,558 data points regarding perceive potential of automation for 2,861 tasks.", "claim": "the survey: 1,640 people employed in Poland scored a representative 2,861 of the 29,753 Polish tasks, 52,558 data points", "modality": "documented", "kind": "fact", "kernel": true, "const": true, "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F12": { "source": "B9", "origin": "own_page", "locus": "§3.3 Survey design and recruitment", "quote": "This means that tasks of managers are assessed by individuals working as managers, tasks of professionals are assessed by professionals, etc.", "claim": "each occupational group's tasks scored by people working in that group", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F13": { "source": "B9", "origin": "own_page", "locus": "§3.1 Initial algorithmic predictions", "quote": "relying on three advanced LLMs available on the market at the end of 2024: GPT-4, GPT-4o and Gemini Flash 1.5.", "claim": "the first, purely algorithmic scoring by three LLMs, GPT-4, GPT-4o and Gemini Flash 1.5", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F14": { "source": "B9", "origin": "own_page", "locus": "§3.1 Initial algorithmic predictions", "quote": "a score ranging from 0 to 1 for each individual task, where 0 means the task cannot be automated and 1 means the task could be fully automated with GenAI technology (the task can be conducted without participation of a human operator).", "claim": "the score defined: 0 a task that cannot be automated, 1 a task that could be fully automated, conducted without a human operator", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "the unit: a task's automation score, an occupation's mean and spread, four gradients", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F15": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶1 s3", "quote": "We then compare this input with a survey and several rounds of Delphi-style discussions among a smaller group of international experts.", "claim": "the workers' scores compared with an expert survey and Delphi-style rounds", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F16": { "source": "B9", "origin": "own_page", "locus": "§4 Expert validation survey ¶1", "quote": "While the experts are expected to have familiarity with the current family of generative AI technologies and their applications, expertise in AI design or development is not a requirement.", "claim": "the experts chosen for knowledge of labour markets and tasks; AI design expertise not required, since technology experts tend to overstate capacities", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F17": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶1 s4", "quote": "Based on this process, we create a repository of knowledge about task automation that goes beyond national specificities and use it to develop an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08.", "claim": "the human scores built into a repository from which an AI assistant predicts scores for ISCO-08's tasks", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F18": { "source": "B9", "origin": "own_page", "locus": "§8 Prediction of synthetic task-level scores", "quote": "the correlation between predictions based on ISCO-08 tasks and Polish 6-digit tasks is 0.92, demonstrating exceptional stability of predictions.", "claim": "predictions from ISCO-08 and from the Polish tasks correlate at 0.92, which the authors call exceptional stability", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F19": { "source": "B9", "origin": "own_page", "locus": "§9 Adjusted Global Index ¶1", "quote": "we rely on two moments of distribution: occupational mean and standard deviation (SD) of task-level scores pertaining to a given 4-digit occupation.", "claim": "an occupation classed by two moments of its task scores, mean and standard deviation", "modality": "stipulation", "kind": "definition", "kernel": true, "const": false, "lineage": "the unit: a task's automation score, an occupation's mean and spread, four gradients", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F20": { "source": "B9", "origin": "own_page", "locus": "§9 ¶3 (the gradients)", "quote": "Gradient 4 highlights occupations with the highest share of tasks exposed to potential GenAI-driven automation, and with a high consistency of this exposure across tasks (Highest exposure, low task variability).", "claim": "Gradient 4 defined: the highest share of tasks exposed to potential automation, consistently across tasks (Table 5: mean ≄ 0.6 and mean − SD ≄ 0.5)", "modality": "stipulation", "kind": "definition", "kernel": false, "const": false, "lineage": "the unit: a task's automation score, an occupation's mean and spread, four gradients", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F21": { "source": "B2", "origin": "card", "locus": "the brief, bullet 3", "quote": "Defines four progressively increasing gradients of GenAI exposure depending on the mean exposure score and the degree of task variability for each ISCO-08 occupation.", "claim": "the brief: four gradients by mean score and task variability", "modality": "stipulation", "kind": "definition", "kernel": false, "const": false, "lineage": "the unit: a task's automation score, an occupation's mean and spread, four gradients", "date": "2025-05-20 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 a3dfbeb78b2a50b8
), whitespace-normalised" }, "F22": { "source": "B9", "origin": "own_page", "locus": "§11 Revised global employment estimates ¶1", "quote": "we focus on the 112 occupations identified as exposed and estimate the share of employment that these occupations represent at the global, regional, and income-based levels.", "claim": "employment shares estimated for the 112 exposed occupations, globally, by region and by income group, on the ILO's global estimation model", "modality": "documented", "kind": "function", "kernel": false, "const": false, "lineage": "the unit: a task's automation score, an occupation's mean and spread, four gradients", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F23": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶3 s1–2", "quote": "Globally, one in four workers are in an occupation with some GenAI exposure. 3.3% of global employment falls into the highest exposure category, albeit with significant differences between female (4.7%) and male employment (2.4%).", "claim": "one in four workers worldwide in an exposed occupation; 3.3% of employment in the highest gradient, 4.7% of female and 2.4% of male", "modality": "documented", "kind": "fact", "kernel": true, "const": true, "lineage": "findings: shares of employment exposed, by income and sex", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F24": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶3 s3", "quote": "These differences increase with countries’ income (9.6% female vs 3.5% male in Gradient 4 in HICs), and so does the overall exposure (11% of total employment in LICs vs 34% in HICs).", "claim": "exposure rises with income, 11% of employment in low-income against 34% in high-income countries; Gradient 4 in high-income countries 9.6% female, 3.5% male", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "findings: shares of employment exposed, by income and sex", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F25": { "source": "B9", "origin": "own_page", "locus": "§11 ¶2", "quote": "However, when disaggregated by gender, 28% of male employment is exposed, with only 3.5% in gradient 4, compared to 41% of female employment, with 14.4% in gradient 3 and 9.6% in gradient 4.", "claim": "in high-income countries 41% of female against 28% of male employment exposed", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "findings: shares of employment exposed, by income and sex", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F26": { "source": "B9", "origin": "own_page", "locus": "§11 ¶3", "quote": "Gender disparities are also more pronounced in wealthier regions, particularly in Europe and Central Asia, where 39% of female employment falls into one of the four exposure gradients compared to 26% for men.", "claim": "in Europe and Central Asia 39% of female against 26% of male employment exposed", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "findings: shares of employment exposed, by income and sex", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F27": { "source": "B9", "origin": "own_page", "locus": "§11 ¶3", "quote": "These patterns reflect both occupational structures and the extent to which GenAI-exposed roles, such as clerical, financial, and customer service jobs, are concentrated in higher-income countries.", "claim": "the patterns by income and sex reflect occupational structures and where exposed roles are concentrated", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "findings: shares of employment exposed, by income and sex", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F28": { "source": "B5", "origin": "card", "locus": "'Exposure is gendered and worse in wealthier economies' ¶2", "quote": "This reflects the gendered distribution of clerical and customer-facing roles, which are both more exposed and more common among female workers.", "claim": "The People Space: the gender gap reflects the gendered distribution of clerical and customer-facing roles", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "findings: shares of employment exposed, by income and sex", "date": "2025-06-04 (post date)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 22c31808fedd7a20
), whitespace-normalised" }, "F29": { "source": "B5", "origin": "card", "locus": "'Location matters. So does income.' ¶2", "quote": "This difference is shaped by infrastructure, digital access and workforce structure.", "claim": "The People Space: the income difference is shaped by infrastructure, digital access and workforce structure", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "findings: shares of employment exposed, by income and sex", "date": "2025-06-04 (post date)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 22c31808fedd7a20
), whitespace-normalised" }, "F30": { "source": "B9", "origin": "own_page", "locus": "§11 ¶4", "quote": "In addition, the 2023 estimate attributed 13% of employment as subject to “augmentation” and an additional 16.2% of employment in “the big unknown”. The combined employment share for Gradients 1-3 for the world in the current analysis is 20.5%, 9 percentage points lower than combined shares of augmentation and “the big unknown” (29.2%) reported in 2023.", "claim": "against 2023: Gradients 1–3 hold 20.5% of world employment, against 2023's 13% augmentation and 16.2% 'big unknown', 29.2% together", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "lineage: the 2023 index", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F31": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶2 s2–3", "quote": "Clerical occupations continue to have the highest exposure levels. Additionally, some strongly digitized occupations have increased exposure, highlighting the expanding abilities of GenAI regarding specialized tasks in professional and technical roles.", "claim": "clerical occupations still the most exposed; some strongly digitized professional and technical occupations more exposed than in 2023", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "findings: which occupations", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F32": { "source": "B9", "origin": "own_page", "locus": "§10 Changes to occupational classifications ¶2", "quote": "Among the 13 occupations classified in gradient 4, the majority represent clerical roles, including data entry clerks, typists and word processing operators, accounting and bookkeeping clerks, and general office clerks.", "claim": "13 occupations in Gradient 4, most of them clerical", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "findings: which occupations", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F33": { "source": "B9", "origin": "own_page", "locus": "Conclusion ¶5", "quote": "some occupations – such as financial analysts, and web and multimedia developers (gradient 4), as well as bank tellers, translators, applications programmers, investment advisers (gradient 3) – have increased their levels of exposure", "claim": "rising: financial analysts and web and multimedia developers (Gradient 4), bank tellers, translators, applications programmers, investment advisers (Gradient 3)", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "findings: which occupations", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F34": { "source": "B9", "origin": "own_page", "locus": "§3.1 Initial algorithmic predictions ¶2", "quote": "the highest exposure to GenAI is typically observed among groups with the largest share of tasks of cognitive nature and it declines with tasks of physical nature, which are more prominent among occupational groups 4-9 (Figure 2).", "claim": "exposure highest where tasks are cognitive; it declines with tasks of physical nature", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "findings: which occupations", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F35": { "source": "B9", "origin": "own_page", "locus": "§10 ¶2", "quote": "our 2023 scores for such tasks, which reached 0.9 in some cases, reflected an overly optimistic assessment of full automation potential. In reality, many of these tasks still require substantial human effort.", "claim": "a revision of its own 2023 scores for clerical tasks: overly optimistic about full automation", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "lineage: the 2023 index", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F36": { "source": "B2", "origin": "card", "locus": "the brief, bullet 4", "quote": "Overall, the automation scores are slightly lower than in 2023 (a mean automation score of 0.29 in 2025 versus 0.30 in 2023), though the variability of scores is considerably lower (standard deviation 0.14 in 2025 v. 0.30 in 2023).", "claim": "mean score 0.29 against 0.30 in 2023; standard deviation 0.14 against 0.30", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "lineage: the 2023 index", "date": "2025-05-20 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 a3dfbeb78b2a50b8
), whitespace-normalised" }, "F37": { "source": "B2", "origin": "card", "locus": "the brief, bullet 5", "quote": "Growing abilities of GenAI models in such areas as voice, image and video generation have increased automation scores for a range of tasks in media- and web-related occupations.", "claim": "scores raised for media- and web-related tasks by voice, image and video generation", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "findings: which occupations", "date": "2025-05-20 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 a3dfbeb78b2a50b8
), whitespace-normalised" }, "F38": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶3 s4", "quote": "As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI.", "claim": "transformation of jobs the most likely impact, since most occupations hold tasks that require human input", "modality": "interpretation", "kind": "interpretation", "kernel": true, "const": true, "lineage": "transformation over redundancy, and its qualification", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F39": { "source": "B2", "origin": "card", "locus": "the brief, bullet 6", "quote": "One in four workers across the world are in an occupation with some degree of GenAI exposure, but because of the continued need for human input, most jobs will be transformed rather than made redundant.", "claim": "the brief: most jobs will be transformed rather than made redundant", "modality": "interpretation", "kind": "prediction", "kernel": true, "const": false, "lineage": "transformation over redundancy, and its qualification", "date": "2025-05-20 (page date)", "quote_ok": true, "checked_against": "saved text B2.txt (sha256 a3dfbeb78b2a50b8
), whitespace-normalised" }, "F40": { "source": "B9", "origin": "own_page", "locus": "§11 ¶6", "quote": "Indeed, the results indicate that few jobs consist of tasks that are fully automatable with current GenAI technology; nearly all occupations have some tasks that require human input.", "claim": "few jobs consist of fully automatable tasks; nearly every occupation has tasks requiring human input", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "transformation over redundancy, and its qualification", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F41": { "source": "B9", "origin": "own_page", "locus": "§11 ¶6", "quote": "However, this does not mean that demand for these occupations will remain stable, as the efficiencies gained from introducing technology may mean that fewer workers are needed.", "claim": "the qualification: demand for these occupations may still fall, since efficiencies may mean fewer workers", "modality": "hypothesis", "kind": "prediction", "kernel": true, "const": false, "lineage": "transformation over redundancy, and its qualification", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F42": { "source": "B4", "origin": "card", "locus": "'How We Built the ILO–NASK Index' ¶4", "quote": "The purpose of the ILO–NASK Index is not to predict the disappearance of jobs, but to identify where work is most likely to change.", "claim": "NASK: the index's purpose is to identify where work is most likely to change, not to predict the disappearance of jobs", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "transformation over redundancy, and its qualification", "date": "undated on the page (© 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 998ee35dd2c9f7eb
), whitespace-normalised" }, "F43": { "source": "B9", "origin": "own_page", "locus": "§11 ¶5", "quote": "It is important to stress that these new estimates reflect “exposure” to GenAI and not the actual impact on employment. As in 2023, the 2025 estimates represent the upper threshold of the percentage of employment that could be affected if GenAI technology were fully implemented.", "claim": "the stated limit: exposure, not the actual impact on employment; an upper threshold, if GenAI were fully implemented", "modality": "self-description", "kind": "function", "kernel": true, "const": true, "lineage": "the stated limits: exposure not impact, an upper threshold, a snapshot", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F44": { "source": "B9", "origin": "own_page", "locus": "§11 ¶5", "quote": "Infrastructure constraints (electricity, broadband), insufficient digital skills, the cost of technology, and inherent operational difficulties associated with the technology are just some of the barriers to full adoption.", "claim": "barriers to full adoption: infrastructure, skills, cost, operational difficulty, and acceptance", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the stated limits: exposure not impact, an upper threshold, a snapshot", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F45": { "source": "B9", "origin": "own_page", "locus": "§11 ¶7", "quote": "Finally, the estimates do not account for new jobs that may be created, nor the technological advances that could potentially automate more tasks in the future. In that sense, our estimates present a static snapshot of the exposure of existing occupations to GenAI at the start of 2025.", "claim": "no new jobs and no future advances counted: a static snapshot at the start of 2025", "modality": "self-description", "kind": "function", "kernel": true, "const": false, "lineage": "the stated limits: exposure not impact, an upper threshold, a snapshot", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F46": { "source": "B9", "origin": "own_page", "locus": "Conclusion ¶7", "quote": "we stress that such exposure does not imply the immediate automation of an entire occupation, but rather the potential for a large share of its current tasks to be performed using this technology.", "claim": "exposure is not the immediate automation of an occupation but the potential for a large share of its tasks", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the stated limits: exposure not impact, an upper threshold, a snapshot", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F47": { "source": "B9", "origin": "own_page", "locus": "§9 ¶5", "quote": "We stress that these classifications merely suggest the general trends and types of occupations at different levels of exposure, since any type of task scoring system carries a degree of subjectivity.", "claim": "the gradients suggest general trends only; any task scoring carries subjectivity", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the stated limits: exposure not impact, an upper threshold, a snapshot", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F48": { "source": "B9", "origin": "own_page", "locus": "footnote 18", "quote": "One of the limitations of the current approach is that neither ISCO-08 nor the Polish classification system includes information on the relative frequency or importance of tasks within occupations. As a result, all tasks are weighted equally when calculating exposure scores.", "claim": "a stated limit: all tasks weighted equally, the classifications carrying no frequency or importance of tasks", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the stated limits: exposure not impact, an upper threshold, a snapshot", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F49": { "source": "B9", "origin": "own_page", "locus": "§3.4 Survey design limitations", "quote": "On the downside, this survey design means that the sample may not represent the entire working population, and interpretability of opinions on the automation potential is limited to comparisons within each 1-digit group.", "claim": "the survey's stated limit: the sample may not represent the working population; opinions comparable only within each 1-digit group", "modality": "self-description", "kind": "function", "kernel": true, "const": false, "lineage": "the stated limits of the survey: Poland, the sample, the match", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F50": { "source": "B9", "origin": "own_page", "locus": "§3.5 ¶8", "quote": "For 91.5% of the tasks, respondents did not report performing a similar task in their work", "claim": "for 91.5% of tasks, respondents did not report performing a similar task", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the stated limits of the survey: Poland, the sample, the match", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F51": { "source": "B9", "origin": "own_page", "locus": "§2 Tasks and occupations in the 6-digit system in Poland ¶1", "quote": "it seems reasonable to assume that Poland is representative of the upper threshold of automation potential, due to high availability of digital equipment and widespread internet access.", "claim": "Poland assumed representative of the upper threshold of automation potential", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "the stated limits of the survey: Poland, the sample, the match", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F52": { "source": "B9", "origin": "own_page", "locus": "Conclusion ¶3", "quote": "The new global index presented in this paper is generic, due to the nature of ISCO-08 and the structure of tasks associated with occupations in this framework.", "claim": "the index is generic, by the nature of ISCO-08; its strength a common global denominator", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the stated limits of the survey: Poland, the sample, the match", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F53": { "source": "B9", "origin": "own_page", "locus": "Conclusion ¶7", "quote": "As argued by numerous economists, the key to unlocking the productivity benefits of GenAI lies not in the search of outright labour savings, but in the extent to which human expertise can be complemented with new technological capacities to reshape occupations and generate additional value-added", "claim": "gains, as economists argue (Acemoglu, Autor): productivity benefits lie in complementing human expertise, not in outright labour savings", "modality": "attributed", "kind": "interpretation", "kernel": true, "const": false, "lineage": "gains, direction and transition: what the index says of them", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F54": { "source": "B9", "origin": "own_page", "locus": "§10 ¶4", "quote": "A key question – largely dependent on the policies shaping this evolution – is to what extent these jobs can retrain and retain existing workers, rather than drive labor churn through workforce replacement and the recruitment of typically younger specialists", "claim": "whether digitized jobs retrain and retain workers or churn them through replacement depends on the policies shaping the change", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "gains, direction and transition: what the index says of them", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F55": { "source": "B9", "origin": "own_page", "locus": "Conclusion ¶8", "quote": "there is need to ensure that the transition is managed through social dialogue, both to minimize “technological unemployment” and the hardship associated with it", "claim": "the transition to be managed through social dialogue, to minimize 'technological unemployment'", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "gains, direction and transition: what the index says of them", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F56": { "source": "B9", "origin": "own_page", "locus": "Conclusion ¶8", "quote": "Workers know their jobs best and can play an important role in the design, adaptation and use of the technology at the workplace, to the benefit of both working conditions and productivity.", "claim": "workers know their jobs best and can shape the technology's design and use", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "gains, direction and transition: what the index says of them", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F57": { "source": "B9", "origin": "own_page", "locus": "Abstract ¶3 s5", "quote": "Linking our refined index with national micro data enables precise projections of such transformations, offering a foundation for social dialogue and targeted policy responses to manage the transition.", "claim": "its use: linked to national microdata, a foundation for social dialogue and policy", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "gains, direction and transition: what the index says of them", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F58": { "source": "B9", "origin": "own_page", "locus": "Conclusion ¶2", "quote": "In addition, we make all task-level scores publicly available, which also makes it possible for researchers to organize the occupational exposure in different ways than our proposed framework of exposure gradients.", "claim": "all task-level scores public (pgmyrek.github.io), so others can organize exposure differently", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "publication and access", "date": "2025-05-20 (WP140)", "quote_ok": true, "checked_against": "saved text B9.txt (sha256 df670a353638d4f5
), whitespace-normalised" }, "F59": { "source": "B4", "origin": "card", "locus": "'Want to explore the full picture?'", "quote": "Download the full dataset (csv)", "claim": "NASK's page offers the full dataset as CSV", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "publication and access", "date": "undated on the page (© 2026)", "quote_ok": true, "checked_against": "saved text B4.txt (sha256 998ee35dd2c9f7eb
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 DOI: https://doi.org/10.54394/HETP0387", "claim": "published 20 May 2025, DOI 10.54394/HETP0387", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "publication and access", "date": "2025-05-20 (page date)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 1da4de837ab8e92f
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 17 March 2026 
 Workers’ exposure to AI: What indicators tell us – and what they don’t Research brief 
 17 April 2026", "claim": "later ILO work on the Observatory: Working Paper 166 (17 March 2026) and a research brief on exposure indicators (17 April 2026)", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "where it lives: the ILO's Observatory and later work", "date": "as fetched 2026-10-29T21:49:12Z (census)", "quote_ok": true, "checked_against": "saved text B10.txt (sha256 6842ee98a74e8d2e
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), whitespace-normalised" }, "F69": { "source": "B5", "origin": "card", "locus": "'Location matters. So does income.' ¶2", "quote": "But it also underscores the risk of a growing global divide in who is most impacted, and who benefits, from GenAI.", "claim": "The People Space: the income gap underscores a divide in who is impacted and who benefits", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "gains, direction and transition: what the index says of them", "date": "2025-06-04 (post date)", "quote_ok": true, "checked_against": "saved text B5.txt (sha256 22c31808fedd7a20
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 in the US has been dropping for years, especially for women,", "claim": "Wikipedia cites WP140 for dropping US enrolment in computing and STEM degrees, especially for women", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "reception: the encyclopedia", "date": "article as fetched 2026-10-29", "quote_ok": true, "checked_against": "saved text B11-cite1.wiki (sha256 719476bdb279532f
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Thomson", "B4": "NASK – PaƄstwowy Instytut Badawczy — ILO-NASK Indeks", "B5": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "B6": "YouTube·International Labour Organization — Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.", "B7": "YouTube·International Labour Organization — How is generative AI reshaping gender inequalities at work?", "B8": "www.scribd.com — Generative AI and Jobs - A Refined Global Index of Occupational Exposure - Scribd", "B9": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "B10": "International Labour Organization — Observatory on AI and Work in the Digital Economy", "B11": "Wikipedia — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (no article; cited in four articles)" }, "delta": { "T_vs_LB": "At 'ILO Generative AI and Jobs index' (T1, eight cards: B1–B8, of which B3 and B8 unreachable and B6 and B7 videos admitted by title; 293 words) the composition gives the index by its title and working-paper number and its headline findings. Composed at the source's grain or coarser: the index as the ILO's updated assessment (F1, F2), the four gradients by mean score and variability (F19–F21), the method's sources, worker surveys, Delphi rounds and AI assessments (F13, F15), 24% and 3.3% (F23), 34% against 11% (F24), transformation over redundancy (F38–F40), clerical occupations and translators most exposed (F31–F33), physical tasks least (F34, coarse). Available on the cards, not composed: every maker but the ILO (NASK's page, F6; the authors, F7), the 2023 paper's title (F10), the date and DOI (F60), the brief and NASK's name for the index (F3, F4), the scores against 2023 (F36), the purpose NASK states (F42), the data and its access (F59, F62), the female and male shares of the highest gradient on card 1 itself (F23's '4.7%' and '2.4%', and F24's by income), the transition through social dialogue on card 1 (F57), the commentary's limit (F68) and its question of who benefits (F69). Limit dropped: on the cards, The People Space's 'exposure does not equal automation' and 'upper-limit scenarios' (F68), while the findings it governs are composed (F23, F24, F38); the paper's own statements of the limit, 'not the actual impact on employment' and the upper threshold (F43), the static snapshot (F45) and its qualification of transformation, that 'fewer workers' may be needed (F41), stand in the own page B9, which T did not surface, and are recorded as not surfaced, not as dropped. Force raised: none observed; transformation is composed as the research's emphasis, at the interpretation's force (F38, F39). Composed without attribution, coarse grain: the reasons for the income and gender gaps, 'differences in economic structures and digital adoption' and women's representation in clerical roles, at the grain of The People Space (F28, F29) and the paper (F27), given as the index's findings without a source named; recorded at the coarse grain of common knowledge. Substituted reading: the gender finding, the paper's 4.7% against 2.4% (F23) replaced by 'roughly 29% vs. 16% across sampled countries', a figure in no source of the field, whose numbers stand in the paper only as the 2023 index's 29.2% augmentation and 'big unknown' together and 16.2% 'big unknown' (F30) (the Capture Registry's finding, CAP-1-03, CAP-1-04, names the 29.2% and not the 16.2%); the two counts, 29,753 tasks and 52,558 data points (F11), joined as 'nearly 30,000 to over 52,000 individual task data points' and placed in ISCO-08 (CAP-1-05; The People Space's 'nearly 30,000 real-world tasks, more than 50,000 human assessments', F66, keeps the two as two measures in one sentence). Content replaced by use: the closer, three prompts, one to 'Policy recommendations from the ILO for workforce upskilling', a recommendation the field does not hold (T1-11). Outside the cards: the gender figure, the least-exposed occupations, 'secretaries, receptionists', 'mass redundancy' (alignment.T1_outside_cards).", "T_vs_LB_reading": "v0.9 §0.5: the first diff is ontological and narrow. T and the field agree on the entity, the ILO's index of occupational exposure, at its headline grain. What T's composition does with its own cards is to keep the findings and drop the statement that bounds them there, The People Space's 'exposure does not equal automation' (F68); the paper's fuller statement of the bound, its snapshot and its doubt about demand (F43, F45, F41) stand on the own page T did not surface. Its ontology at work is that of a finding without its bound: an exposure estimate composed as a fact about jobs. Where the cards are thin it supplies figures and lists no field source gives: a gender ratio (T1-6) and least-exposed occupations (T1-8). The makers reduce to the ILO: NASK, the preparer on its own page (card 4, F6), does not appear, nor any author (card 1, F7).", "LB_vs_LBA": "Admission adds, in one claim space with the field's: the record's organizing question and its placing of the index at the intersection of gains and losses, as an exposure estimate (LV-05–LV-08); the record's answer to the question for this index, the labor object and who supplies, directs and gains, each 'not recorded' as observed (LV-10–LV-13); the archive's reading that the index estimates where task labor could pass to a GenAI system on technical feasibility, meets task-origin only as a prior, and observes no performed work, so cannot speak to a system's labor on a commissioned task or to user labor drag (LV-34, LV-35); what the evidence settles and what it does not (LV-28–LV-33), with the worked question that sets an exposure estimate against observed trackers and the gap that no record establishes exposure as the cause of employment change (LV-48–LV-50); the record's negative tags (LV-41–LV-44); on the class of the employment effects of AI, the archive's statements that AI integrates the means of semantic production and automates its labor under concentrated ownership (#499), that labor platforms displace is relocated and enclosed, not vanished (#533), and that an account of AI job loss narrated as mechanism is a scenario and not a prediction, with a redistributive resolution (#521, #520); and the Capture Registry's findings at the name (CAP-1-01–CAP-1-11). The field's claims are carried whole and composed as received; none is translated (equal-terms case). The archive's additions extend the field's object on its own terms: the paper's limits (F43, F45, F46) and its qualification (F41) are what the record's negative tags and does-not-answer fields (LV-41–LV-44, LV-30–LV-33) restate at the record's grain, and the record adds the question the paper does not ask of itself, whose labor and to whose gain, answered 'not recorded'.", "alignment": { "T1_cards_claims": [ "F1", "F2", "F3", "F4", "F6", "F7", "F10", "F11", "F15", "F17", "F21", "F23", "F24", "F28", "F29", "F31", "F36", "F37", "F38", "F39", "F42", "F57", "F59", "F60", "F62", "F65", "F66", "F67", "F68", "F69", "F70", "F71" ], "T1_composed": [ "F1", "F2", "F13", "F15", "F19", "F20", "F21", "F23", "F24", "F27", "F28", "F29", "F31", "F32", "F33", "F34", "F38", "F39", "F40", "F66" ], "T1_outside_cards": [ "'roughly 29% vs. 16% across sampled countries' and 'nearly twice as likely' (no field source; the paper's 29.2% and 16.2% are 2023 category shares, F30, and its female/male figures are 4.7%/2.4% globally and 41%/28% exposed in high-income countries, F23, F25)", "'secretaries, receptionists' among the most exposed and 'routine information-processing roles' (no field source in these words; the paper names administrative secretaries in its Conclusion, outside the claims ledgered)", "'Manual trades, construction, agriculture, and heavy manufacturing' as least exposed (no field source names them; F34 states only that exposure declines with tasks of physical nature)", "'mass redundancy' (no field source)", "'Policy recommendations from the ILO for workforce upskilling?' (no field source states an upskilling recommendation; the paper asks for social dialogue, F55)" ], "rule": "a field claim counts as on T's cards when its source is a card or its quote occurs in a card's saved text or snippet (card 1, the ILO's publication page, carries the paper's abstract verbatim); it 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)" } }, "kernel": [ { "K": "K1", "claim": "`LV-01`", "source": "Labor Vector record", "M_src": "stipulation", "sense": "what it is: the ILO's index of occupational exposure to GenAI, refined", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F1, F2, F3, F4", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K2", "claim": "`LV-02`", "source": "Labor Vector record", "M_src": "stipulation", "sense": "what it is: the ILO's index of occupational exposure to GenAI, refined", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F1, F2, F3, F4)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K3", "claim": "`LV-03`", "source": "Labor Vector record", "M_src": "documented", "sense": "what it is: the ILO's index of occupational exposure to GenAI, refined", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F1, F2, F3, F4", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K4", "claim": "`LV-05`", "source": "Labor Vector record", "M_src": "stipulation", "sense": "the record's question: whose labor, directed by whom, to whose gain, on what evidence", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K5", "claim": "`LV-06`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: gains and losses, an exposure estimate", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K6", "claim": "`LV-07`", "source": "Labor Vector record", "M_src": "stipulation", "sense": "the record's placing: gains and losses, an exposure estimate", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K7", "claim": "`LV-08`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: gains and losses, an exposure estimate", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K8", "claim": "`LV-04`", "source": "Labor Vector record", "M_src": "documented", "sense": "the makers: the ILO, NASK and the authors", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F5, F6, F7, F8)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K9", "claim": "`LV-39`", "source": "Labor Vector record", "M_src": "documented", "sense": "lineage: the 2023 index", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F9, F10, F30, F35, F36)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K10", "claim": "`LV-16`", "source": "Labor Vector record", "M_src": "documented", "sense": "method: workers scoring tasks, experts, LLMs, a predictor", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F11, F12, F13, F15, F16, F17)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K11", "claim": "`CAP-1-05`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name: two counts run together", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "rival claim: T1-9 against F11", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K12", "claim": "`LV-14`", "source": "Labor Vector record", "M_src": "documented", "sense": "method: workers scoring tasks, experts, LLMs, a predictor", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F11, F12, F13, F15, F16, F17)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K13", "claim": "`LV-09`", "source": "Labor Vector record", "M_src": "stipulation", "sense": "the unit: a task's automation score, an occupation's mean and spread, four gradients", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F14, F19, F20, F21, F22)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K14", "claim": "`LV-17`", "source": "Labor Vector record", "M_src": "documented", "sense": "findings: shares of employment exposed, by income and sex", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F23, F24, F25, F26, F27, F28)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K15", "claim": "`LV-18`", "source": "Labor Vector record", "M_src": "documented", "sense": "findings: shares of employment exposed, by income and sex", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F23, F24, F25, F26, F27, F28)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K16", "claim": "`CAP-1-01`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name: what it held", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K17", "claim": "`CAP-1-02`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name: what it held", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K18", "claim": "`CAP-1-08`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name: what it held", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K19", "claim": "`CAP-1-11`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name: what it held", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K20", "claim": "`LV-19`", "source": "Labor Vector record", "M_src": "documented", "sense": "findings: shares of employment exposed, by income and sex", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F23, F24, F25, F26, F27, F28)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K21", "claim": "`CAP-1-03`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name: a figure not in the paper", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "rival claim: T1-6 (the composition's gender figure) against F23, F25", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K22", "claim": "`CAP-1-04`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name: a figure not in the paper", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "rival claim: T1-6 against F30 (the 2023 shares in the paper)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K23", "claim": "`LV-20`", "source": "Labor Vector record", "M_src": "documented", "sense": "findings: which occupations", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F31, F32, F33, F34, F37)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K24", "claim": "`LV-21`", "source": "Labor Vector record", "M_src": "documented", "sense": "lineage: the 2023 index", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F9, F10, F30, F35, F36)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K25", "claim": "`LV-22`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "transformation over redundancy, and its qualification", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F38, F39, F40, F41, F42)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K26", "claim": "`O499-01`", "source": "#499", "M_src": "interpretation", "sense": "the archive on the class: AI automating labor, and who owns its means", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K27", "claim": "`O499-02`", "source": "#499", "M_src": "interpretation", "sense": "the archive on the class: AI automating labor, and who owns its means", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K28", "claim": "`O533-01`", "source": "#533", "M_src": "interpretation", "sense": "the archive on the class: displaced labor relocated, and whose gain", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F38, F39, F40 (transformation over redundancy; the archive: displaced labor relocated, not vanished)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K29", "claim": "`O533-02`", "source": "#533", "M_src": "interpretation", "sense": "the archive on the class: displaced labor relocated, and whose gain", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K30", "claim": "`LV-23`", "source": "Labor Vector record", "M_src": "self-description", "sense": "the stated limits: exposure not impact, an upper threshold, a snapshot", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F43, F44, F45, F46, F47, F48)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K31", "claim": "`LV-24`", "source": "Labor Vector record", "M_src": "self-description", "sense": "the stated limits: exposure not impact, an upper threshold, a snapshot", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F43, F44, F45, F46, F47, F48)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K32", "claim": "`LV-15`", "source": "Labor Vector record", "M_src": "documented", "sense": "the stated limits: exposure not impact, an upper threshold, a snapshot", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F43, F44, F45, F46, F47, F48)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K33", "claim": "`LV-25`", "source": "Labor Vector record", "M_src": "self-description", "sense": "the stated limits of the survey: Poland, the sample, the match", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F49, F50, F51, F52)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K34", "claim": "`LV-26`", "source": "Labor Vector record", "M_src": "documented", "sense": "the stated limits of the survey: Poland, the sample, the match", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F49, F50, F51, F52)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K35", "claim": "`LV-27`", "source": "Labor Vector record", "M_src": "self-description", "sense": "the stated limits of the survey: Poland, the sample, the match", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F49, F50, F51, F52)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K36", "claim": "`LV-41`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's negative tags", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K37", "claim": "`LV-42`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's negative tags", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F38, F39, F42 (the record: not a forecast of job loss)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K38", "claim": "`LV-43`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's negative tags", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K39", "claim": "`LV-44`", "source": "Labor Vector record", "M_src": "documented", "sense": "the record's negative tags", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K40", "claim": "`CAP-1-06`", "source": "Capture Registry, 2026-10-29", "M_src": "documented", "sense": "the composition at the name: the limits absent", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F43, F45, F49 (the limits, absent from T)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K41", "claim": "`CAP-1-07`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name: the limits absent", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K42", "claim": "`CAP-1-09`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the composition at the name: the limits absent", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K43", "claim": "`CAP-1-10`", "source": "Capture Registry, 2026-10-29", "M_src": "interpretation", "sense": "the record's negative tags", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K44", "claim": "`LV-10`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "direction: who supplies, directs and gains (the record's reading)", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain ()", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K45", "claim": "`LV-11`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "direction: who supplies, directs and gains (the record's reading)", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain ()", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K46", "claim": "`LV-12`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "direction: who supplies, directs and gains (the record's reading)", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K47", "claim": "`LV-13`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "direction: who supplies, directs and gains (the record's reading)", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain ()", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K48", "claim": "`LV-34`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the archive's reading: feasibility against performed work", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K49", "claim": "`LV-35`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the archive's reading: feasibility against performed work", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K50", "claim": "`LV-28`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K51", "claim": "`LV-29`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K52", "claim": "`LV-30`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K53", "claim": "`LV-31`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F38, F39 (transformation the most likely impact; the record: whether exposure leads to job loss, transformation or new work is not answered)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K54", "claim": "`LV-32`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K55", "claim": "`LV-33`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K56", "claim": "`LV-48`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K57", "claim": "`LV-49`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K58", "claim": "`LV-50`", "source": "Labor Vector record", "M_src": "interpretation", "sense": "the record's placing: what the evidence can and cannot settle", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K59", "claim": "`O521-01`", "source": "#521", "M_src": "interpretation", "sense": "the archive on the class: displacement accounts as scenario, not forecast", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F42 (NASK: not to predict the disappearance of jobs; the archive: displacement accounts as scenario)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K60", "claim": "`O520-01`", "source": "#520", "M_src": "interpretation", "sense": "the archive on the class: displacement accounts as scenario, not forecast", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K61", "claim": "`LV-36`", "source": "Labor Vector record", "M_src": "documented", "sense": "publication and access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F58, F59, F60, F61, F62)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K62", "claim": "`LV-37`", "source": "Labor Vector record", "M_src": "documented", "sense": "publication and access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F58, F59, F60, F61, F62)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K63", "claim": "`LV-38`", "source": "Labor Vector record", "M_src": "documented", "sense": "where it lives: the ILO's Observatory and later work", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F63, F64)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K64", "claim": "`LV-40`", "source": "Labor Vector record", "M_src": "documented", "sense": "publication and access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "none: the same claim as the field's at the record's grain (F58, F59, F60, F61, F62)", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K65", "claim": "`LV-45`", "source": "Labor Vector record", "M_src": "documented", "sense": "where it lives: the ILO's Observatory and later work", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F63, F64", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K66", "claim": "`LV-46`", "source": "Labor Vector record", "M_src": "self-description", "sense": "where it lives: the ILO's Observatory and later work", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F63, F64", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" }, { "K": "K67", "claim": "`LV-47`", "source": "Labor Vector record", "M_src": "self-description", "sense": "publication and access", "qualifiers carried": "—", "f (source's own falsifiers)": "— (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake §8.3)", "contrast": "qualified claim: F58, F59, F60, F61, F62", "resolver": "a later capture at 'ILO Generative AI and Jobs index' (ADDR-fb3510e08ef5) on Google AI Overview, seated in the Capture Registry (§8.3)" } ], "kernel_T": [ { "claim": "F68", "note": "the limit on card 5 (The People Space, 'exposure does not equal automation', 'upper-limit scenarios'), absent while the findings it governs are composed" }, { "claim": "F23", "note": "the female and male shares of the highest gradient (4.7%, 2.4%), on card 1; replaced by a figure in no source" }, { "claim": "F24", "note": "Gradient 4 in high-income countries by sex (9.6%, 3.5%), on card 1; absent" }, { "claim": "F6", "note": "NASK's page: the report prepared by NASK's office, card 4; absent" }, { "claim": "F7", "note": "the authors, card 1; absent" }, { "claim": "F10", "note": "the 2023 paper's title on card 6; absent" }, { "claim": "F60", "note": "date and DOI on card 1; absent" }, { "claim": "F36", "note": "the scores against 2023 on card 2; absent" }, { "claim": "F42", "note": "NASK's statement of purpose on card 4; absent" }, { "claim": "F57", "note": "the paper's use for social dialogue and policy, on card 1; absent (the closer offers 'upskilling')" }, { "claim": "F69", "note": "who is impacted and who benefits, card 5; absent" }, { "claim": "F11", "note": "the two counts kept apart; joined" } ], "ledger": { "archive": "datasets/negative-of-the-negative/v2/ledgers/ilo-genai-exposure-index/ledger-archive.json", "field": "datasets/negative-of-the-negative/v2/ledgers/ilo-genai-exposure-index/field.json", "selection": "datasets/negative-of-the-negative/v2/traversal/ilo-genai-exposure-index/", "config": "datasets/negative-of-the-negative/v2/panel/configs/ilo-genai-exposure-index.json", "intake": "datasets/negative-of-the-negative/v2/intake/ilo-genai-exposure-index-20261029/", "audit": null }, "ontology": { "composition": "equal", "composition_note": "equal-terms case (v0.9 §0.9, §4.1): one claim space; the field's and the archive's claims ordered together by the entity's dependency and by genealogy within each sense; every field claim composed as received, beside the archive claims it stands with", "case": { "value": "equal", "statement": "The archive reads the field's entity as the entity. The Labor Vector record describes the index from its own pages, each field with a verbatim quote, and its additions are senses and limits on the same object: the record's question of supply, direction and gains, the distinction between feasibility and performed work drawn with the archive's instrument of labor direction, and the class claims on displacement. No ontology here is one the field cannot hold: the index's own limits already state that exposure is not impact, and the archive's reading extends that statement to the question of whose labor. Equal-terms case (§0.9): one claim space, nothing translated, neither ledger placed first.", "loci": [ { "dep": 771, "locus": "Disambiguation Matrix, POSITIVE 1", "quote": "POSITIVE: Directionality of semantic labor measures the direction of a system's labor relative to the commissioned task across five layers (C, T, R, O, A)." }, { "dep": 771, "locus": "Aphoristic Tooth", "quote": "Provenance is not only claim-origin; provenance is task-origin." }, { "dep": 499, "locus": "§2.5 ¶1", "quote": "increasingly automates the labor (generation replacing human semantic labor)" }, { "dep": 533, "locus": "'Labor bearing-cost'", "quote": "He does not note that the labor does not vanish. It is *relocated* — from visible human interpreters to invisible architectural constraints." }, { "dep": 521, "locus": "¶10", "quote": "What follows is a scenario, not a prediction." } ] }, "E_F": { "type": "C (public entity: an index of occupational exposure published as a working paper, with its research brief)", "definition": "the ILO's refined global index of occupational exposure to generative AI (Working Paper 140, 20 May 2025, doi:10.54394/HETP0387, CC BY 4.0), made by the ILO's Research department with NASK, which updates the ILO's 2023 index: task-level automation scores from LLMs, a survey of 1,640 workers in Poland, expert review and an AI predictor, aggregated to ISCO-08 occupations by mean and spread into four exposure gradients, with employment shares by region, income and sex (one in four workers exposed; 3.3% in Gradient 4); transformation its most likely impact; its stated limits exposure not impact, an upper threshold, a static snapshot, a survey in Poland", "claims": [ "F1", "F2", "F4", "F5", "F9", "F11", "F14", "F19", "F21", "F23", "F38", "F41", "F43", "F45", "F49", "F60", "F61" ] }, "E_A": { "definition": "the same index, in the Labor Vector census record and the archive's deposits: an exposure estimate placed at the intersection of the distribution of gains and losses, under the question whose labor, directed by whom, to whose gain; it estimates where task labor could pass to a GenAI system on grounds of technical feasibility, meets task-origin only as a prior on which tasks are exposed, and observes no performed work, so cannot speak to a system's labor on a commissioned task or to user labor drag; who supplies, directs and gains it records as 'not recorded' as observed; what it settles (which tasks are scored feasible, what share of employment is exposed) is kept apart from what it does not (observed change, causation, direction, gains, use); of its class, the archive holds that AI integrates the means of semantic production and automates its labor under concentrated ownership, that displaced labor is relocated and enclosed, not vanished, and that an account of AI job loss narrated as mechanism is a scenario with a redistributive resolution", "claims": [ "LV-02", "LV-05", "LV-06", "LV-07", "LV-08", "LV-10", "LV-11", "LV-12", "LV-13", "LV-28", "LV-30", "LV-34", "LV-35", "LV-41", "LV-42", "LV-49", "O499-01", "O533-01", "O521-01", "O520-01" ], "defining_papers": [ 499, 520, 521, 533 ], "defining_records": [ "Labor Vector meta-aggregator record, https://www.laborvector.org/aggregators/ilo-genai-exposure-index/ (census.json rows[slug == 'ilo-genai-exposure-index'], organizing_question, intersections; questions.json 'observed-or-estimated'), editor Lee Sharks, 2026-10-29", "Capture Registry entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.đŸ”Žâ™‹đŸ”€âœ‹âïžđŸŒ ), 2026-10-29" ] }, "rho": { "value": "extends", "status": "read from the Labor Vector census record, the capture record and the deposits that bear on the index's class (§1.2a); no deposit names the index; the record's lv_intersection is the archive's reading of the entity, its terms (task-origin, a commissioned task, user labor drag) the Directionality of Semantic Labor's (#771), read for them", "statement": "The archive reads the ILO index as its own sources present it (every field of the census record carries a verbatim quote from the paper, the brief or the Observatory) and adds, on the field's own object: the question the paper does not put to itself, whose labor, directed by whom, to whose gain, answered 'not recorded' as observed (LV-05, LV-10–LV-13); the distinction between technical feasibility and performed work, by which the index meets task-origin only as a prior and cannot speak to a system's labor on a commissioned task or to user labor drag (LV-34, LV-35); what the evidence settles and what it does not, an estimate set against observed trackers with no record establishing exposure as a cause (LV-28–LV-33, LV-48–LV-50); the negative tags (LV-41–LV-44); of the class, the automation of semantic labor under concentrated ownership (#499), displaced labor relocated and enclosed (#533), and the account of job loss as scenario with a redistributive resolution (#521, #520); and the composition's departures at the name (CAP-1-03–CAP-1-07).", "claims": [ "LV-05", "LV-10", "LV-11", "LV-12", "LV-13", "LV-34", "LV-35", "LV-28", "LV-30", "LV-31", "LV-32", "LV-49", "LV-50", "O499-01", "O533-01", "O521-01", "O520-01" ], "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 against a field claim (the record's eight authors against the paper's seven; NASK as expert group against NASK as preparer; transformation against displaced labor relocated), both are composed under P_coh within the one ontology.", "considered": [ { "value": "constructs", "rejected": "the archive does not read the index as made by reception; its record takes the paper, the brief and the Observatory as the entity's own account" }, { "value": "revises", "rejected": "the index is not the archive's own entity; there is no earlier archive statement of it to keep, narrow or withdraw" } ] }, "translation": [ { "lineage": "what it is: the ILO's index of occupational exposure to GenAI, refined", "field_claims": [ "F1", "F2", "F3", "F4" ], "received": "F1 (B9, Abstract ¶1 s1): \"This study updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI), incorporating recent advances in the technology and increasing user familiarity with GenAI tools.\"; F2 (B1, the publication page, lede): \"This ILO Working Paper refines the global measurement of occupational exposure to generative AI by combining task-level data, expert input, and AI model predictions.\"; F3 (B2, the brief's page, heading and lede): \"Research Brief Generative AI and jobs: A 2025 update This brief summarizes an ILO Working paper that refines the global assessment of occupational exposure to generative AI.\"; F4 (B4, 'How We Built the ILO–NASK Index' ¶1): \"The ILO–NASK Index was developed to better understand how generative AI may affect work at the task level.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-01, LV-02, LV-03", "archive_claims": [ "LV-01", "LV-02", "LV-03" ] }, { "lineage": "the makers: the ILO, NASK and the authors", "field_claims": [ "F5", "F6", "F7", "F8" ], "received": "F5 (B9, Introduction ¶3): \"the Research department of the ILO has partnered with NASK, the National Research Institute of the Ministry of Digital Affairs in Poland, to develop a more precise assessment of potential effects of GenAI on employment across countries.\"; F6 (B4, 'Authors Who prepared the report?'): \"Authors Who prepared the report? NASK Analysis and Research Office 
 it played an active role in the preparation of this report.\"; F7 (B1, 'Additional details', Author(s)): \"Author(s) Pawel Gmyrek Janine Berg Karol KamiƄski Filip KonopczyƄski Agnieszka Ɓadna Balint Nafradi Konrad RosƂaniec Marek TroszyƄski\"; F8 (B9, title block): \"PaweƂ Gmyrek Janine Berg Karol KamiƄski Filip KonopczyƄski Agnieszka Ɓadna Konrad RosƂaniec Marek TroszyƄski Abstract\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-04", "archive_claims": [ "LV-04" ] }, { "lineage": "lineage: the 2023 index", "field_claims": [ "F9", "F10", "F30", "F35", "F36" ], "received": "F9 (B9, Introduction ¶4): \"This study builds on the method developed by the ILO (Gmyrek, Berg and Bescond, 2023 - GBB hereafter) , which used task descriptions from the 4-digit International Standard Classification of Occupations (ISCO-08) and the GPT-4 model to estimate task-level and occupational scores of exposure to GenAI technology\"; F10 (B6, card title (video, 11m; unreachable: no URL found)): \"Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.\"; F30 (B9, §11 ¶4): \"In addition, the 2023 estimate attributed 13% of employment as subject to “augmentation” and an additional 16.2% of employment in “the big unknown”. The combined employment share for Gradients 1-3 for the world in the current analysis is 20.5%, 9 percentage points lower than combined shares of augmentation and “the big unknown” (29.2%) reported in 2023.\"; F35 (B9, §10 ¶2): \"our 2023 scores for such tasks, which reached 0.9 in some cases, reflected an overly optimistic assessment of full automation potential. In reality, many of these tasks still require substantial human effort.\"; F36 (B2, the brief, bullet 4): \"Overall, the automation scores are slightly lower than in 2023 (a mean automation score of 0.29 in 2025 versus 0.30 in 2023), though the variability of scores is considerably lower (standard deviation 0.14 in 2025 v. 0.30 in 2023).\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-39, LV-21", "archive_claims": [ "LV-39", "LV-21" ] }, { "lineage": "method: workers scoring tasks, experts, LLMs, a predictor", "field_claims": [ "F11", "F12", "F13", "F15", "F16", "F17", "F18" ], "received": "F11 (B9, Abstract ¶1 s2): \"Using a representative sample from the 29,753 tasks in the Polish occupational classification system and a survey of 1,640 people employed in each 1-digit ISCO-08 groups, we collect 52,558 data points regarding perceive potential of automation for 2,861 tasks.\"; F12 (B9, §3.3 Survey design and recruitment): \"This means that tasks of managers are assessed by individuals working as managers, tasks of professionals are assessed by professionals, etc.\"; F13 (B9, §3.1 Initial algorithmic predictions): \"relying on three advanced LLMs available on the market at the end of 2024: GPT-4, GPT-4o and Gemini Flash 1.5.\"; F15 (B9, Abstract ¶1 s3): \"We then compare this input with a survey and several rounds of Delphi-style discussions among a smaller group of international experts.\"; F16 (B9, §4 Expert validation survey ¶1): \"While the experts are expected to have familiarity with the current family of generative AI technologies and their applications, expertise in AI design or development is not a requirement.\"; F17 (B9, Abstract ¶1 s4): \"Based on this process, we create a repository of knowledge about task automation that goes beyond national specificities and use it to develop an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08.\"; F18 (B9, §8 Prediction of synthetic task-level scores): \"the correlation between predictions based on ISCO-08 tasks and Polish 6-digit tasks is 0.92, demonstrating exceptional stability of predictions.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-16, LV-14", "archive_claims": [ "LV-16", "LV-14" ] }, { "lineage": "the unit: a task's automation score, an occupation's mean and spread, four gradients", "field_claims": [ "F14", "F19", "F20", "F21", "F22" ], "received": "F14 (B9, §3.1 Initial algorithmic predictions): \"a score ranging from 0 to 1 for each individual task, where 0 means the task cannot be automated and 1 means the task could be fully automated with GenAI technology (the task can be conducted without participation of a human operator).\"; F19 (B9, §9 Adjusted Global Index ¶1): \"we rely on two moments of distribution: occupational mean and standard deviation (SD) of task-level scores pertaining to a given 4-digit occupation.\"; F20 (B9, §9 ¶3 (the gradients)): \"Gradient 4 highlights occupations with the highest share of tasks exposed to potential GenAI-driven automation, and with a high consistency of this exposure across tasks (Highest exposure, low task variability).\"; F21 (B2, the brief, bullet 3): \"Defines four progressively increasing gradients of GenAI exposure depending on the mean exposure score and the degree of task variability for each ISCO-08 occupation.\"; F22 (B9, §11 Revised global employment estimates ¶1): \"we focus on the 112 occupations identified as exposed and estimate the share of employment that these occupations represent at the global, regional, and income-based levels.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-09", "archive_claims": [ "LV-09" ] }, { "lineage": "findings: shares of employment exposed, by income and sex", "field_claims": [ "F23", "F24", "F25", "F26", "F27", "F28", "F29", "F71" ], "received": "F23 (B9, Abstract ¶3 s1–2): \"Globally, one in four workers are in an occupation with some GenAI exposure. 3.3% of global employment falls into the highest exposure category, albeit with significant differences between female (4.7%) and male employment (2.4%).\"; F24 (B9, Abstract ¶3 s3): \"These differences increase with countries’ income (9.6% female vs 3.5% male in Gradient 4 in HICs), and so does the overall exposure (11% of total employment in LICs vs 34% in HICs).\"; F25 (B9, §11 ¶2): \"However, when disaggregated by gender, 28% of male employment is exposed, with only 3.5% in gradient 4, compared to 41% of female employment, with 14.4% in gradient 3 and 9.6% in gradient 4.\"; F26 (B9, §11 ¶3): \"Gender disparities are also more pronounced in wealthier regions, particularly in Europe and Central Asia, where 39% of female employment falls into one of the four exposure gradients compared to 26% for men.\"; F27 (B9, §11 ¶3): \"These patterns reflect both occupational structures and the extent to which GenAI-exposed roles, such as clerical, financial, and customer service jobs, are concentrated in higher-income countries.\"; F28 (B5, 'Exposure is gendered and worse in wealthier economies' ¶2): \"This reflects the gendered distribution of clerical and customer-facing roles, which are both more exposed and more common among female workers.\"; F29 (B5, 'Location matters. So does income.' ¶2): \"This difference is shaped by infrastructure, digital access and workforce structure.\"; F71 (B7, card title (video, 3m; unreachable: no URL found)): \"How is generative AI reshaping gender inequalities at work?\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-17, LV-18, LV-19", "archive_claims": [ "LV-17", "LV-18", "LV-19" ] }, { "lineage": "findings: which occupations", "field_claims": [ "F31", "F32", "F33", "F34", "F37" ], "received": "F31 (B9, Abstract ¶2 s2–3): \"Clerical occupations continue to have the highest exposure levels. Additionally, some strongly digitized occupations have increased exposure, highlighting the expanding abilities of GenAI regarding specialized tasks in professional and technical roles.\"; F32 (B9, §10 Changes to occupational classifications ¶2): \"Among the 13 occupations classified in gradient 4, the majority represent clerical roles, including data entry clerks, typists and word processing operators, accounting and bookkeeping clerks, and general office clerks.\"; F33 (B9, Conclusion ¶5): \"some occupations – such as financial analysts, and web and multimedia developers (gradient 4), as well as bank tellers, translators, applications programmers, investment advisers (gradient 3) – have increased their levels of exposure\"; F34 (B9, §3.1 Initial algorithmic predictions ¶2): \"the highest exposure to GenAI is typically observed among groups with the largest share of tasks of cognitive nature and it declines with tasks of physical nature, which are more prominent among occupational groups 4-9 (Figure 2).\"; F37 (B2, the brief, bullet 5): \"Growing abilities of GenAI models in such areas as voice, image and video generation have increased automation scores for a range of tasks in media- and web-related occupations.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-20", "archive_claims": [ "LV-20" ] }, { "lineage": "transformation over redundancy, and its qualification", "field_claims": [ "F38", "F39", "F40", "F41", "F42" ], "received": "F38 (B9, Abstract ¶3 s4): \"As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI.\"; F39 (B2, the brief, bullet 6): \"One in four workers across the world are in an occupation with some degree of GenAI exposure, but because of the continued need for human input, most jobs will be transformed rather than made redundant.\"; F40 (B9, §11 ¶6): \"Indeed, the results indicate that few jobs consist of tasks that are fully automatable with current GenAI technology; nearly all occupations have some tasks that require human input.\"; F41 (B9, §11 ¶6): \"However, this does not mean that demand for these occupations will remain stable, as the efficiencies gained from introducing technology may mean that fewer workers are needed.\"; F42 (B4, 'How We Built the ILO–NASK Index' ¶4): \"The purpose of the ILO–NASK Index is not to predict the disappearance of jobs, but to identify where work is most likely to change.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-22", "archive_claims": [ "LV-22" ] }, { "lineage": "the stated limits: exposure not impact, an upper threshold, a snapshot", "field_claims": [ "F43", "F44", "F45", "F46", "F47", "F48", "F68" ], "received": "F43 (B9, §11 ¶5): \"It is important to stress that these new estimates reflect “exposure” to GenAI and not the actual impact on employment. As in 2023, the 2025 estimates represent the upper threshold of the percentage of employment that could be affected if GenAI technology were fully implemented.\"; F44 (B9, §11 ¶5): \"Infrastructure constraints (electricity, broadband), insufficient digital skills, the cost of technology, and inherent operational difficulties associated with the technology are just some of the barriers to full adoption.\"; F45 (B9, §11 ¶7): \"Finally, the estimates do not account for new jobs that may be created, nor the technological advances that could potentially automate more tasks in the future. In that sense, our estimates present a static snapshot of the exposure of existing occupations to GenAI at the start of 2025.\"; F46 (B9, Conclusion ¶7): \"we stress that such exposure does not imply the immediate automation of an entire occupation, but rather the potential for a large share of its current tasks to be performed using this technology.\"; F47 (B9, §9 ¶5): \"We stress that these classifications merely suggest the general trends and types of occupations at different levels of exposure, since any type of task scoring system carries a degree of subjectivity.\"; F48 (B9, footnote 18): \"One of the limitations of the current approach is that neither ISCO-08 nor the Polish classification system includes information on the relative frequency or importance of tasks within occupations. As a result, all tasks are weighted equally when calculating exposure scores.\"; F68 (B5, 'Caution, not panic: exposure is not impact' ¶1): \"A critical distinction in the ILO’s analysis is that exposure does not equal automation. These are best-case, upper-limit scenarios – estimates of what could be done with GenAI, not what will be.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-23, LV-24, LV-15", "archive_claims": [ "LV-23", "LV-24", "LV-15" ] }, { "lineage": "the stated limits of the survey: Poland, the sample, the match", "field_claims": [ "F49", "F50", "F51", "F52" ], "received": "F49 (B9, §3.4 Survey design limitations): \"On the downside, this survey design means that the sample may not represent the entire working population, and interpretability of opinions on the automation potential is limited to comparisons within each 1-digit group.\"; F50 (B9, §3.5 ¶8): \"For 91.5% of the tasks, respondents did not report performing a similar task in their work\"; F51 (B9, §2 Tasks and occupations in the 6-digit system in Poland ¶1): \"it seems reasonable to assume that Poland is representative of the upper threshold of automation potential, due to high availability of digital equipment and widespread internet access.\"; F52 (B9, Conclusion ¶3): \"The new global index presented in this paper is generic, due to the nature of ISCO-08 and the structure of tasks associated with occupations in this framework.\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-25, LV-26, LV-27", "archive_claims": [ "LV-25", "LV-26", "LV-27" ] }, { "lineage": "gains, direction and transition: what the index says of them", "field_claims": [ "F53", "F54", "F55", "F56", "F57", "F69", "F70" ], "received": "F53 (B9, Conclusion ¶7): \"As argued by numerous economists, the key to unlocking the productivity benefits of GenAI lies not in the search of outright labour savings, but in the extent to which human expertise can be complemented with new technological capacities to reshape occupations and generate additional value-added\"; F54 (B9, §10 ¶4): \"A key question – largely dependent on the policies shaping this evolution – is to what extent these jobs can retrain and retain existing workers, rather than drive labor churn through workforce replacement and the recruitment of typically younger specialists\"; F55 (B9, Conclusion ¶8): \"there is need to ensure that the transition is managed through social dialogue, both to minimize “technological unemployment” and the hardship associated with it\"; F56 (B9, Conclusion ¶8): \"Workers know their jobs best and can play an important role in the design, adaptation and use of the technology at the workplace, to the benefit of both working conditions and productivity.\"; F57 (B9, Abstract ¶3 s5): \"Linking our refined index with national micro data enables precise projections of such transformations, offering a foundation for social dialogue and targeted policy responses to manage the transition.\"; F69 (B5, 'Location matters. So does income.' ¶2): \"But it also underscores the risk of a growing global divide in who is most impacted, and who benefits, from GenAI.\"; F70 (B5, 'What next?' ¶2): \"“The key to unlocking the productivity benefits of GenAI lies not in the search for labour savings, but in the extent to which human expertise can be complemented by new technological capacities.”\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] }, { "lineage": "publication and access", "field_claims": [ "F58", "F59", "F60", "F61", "F62" ], "received": "F58 (B9, Conclusion ¶2): \"In addition, we make all task-level scores publicly available, which also makes it possible for researchers to organize the occupational exposure in different ways than our proposed framework of exposure gradients.\"; F59 (B4, 'Want to explore the full picture?'): \"Download the full dataset (csv)\"; F60 (B1, 'Publication details' and 'References'): \"Date 20 May 2025 
 DOI: https://doi.org/10.54394/HETP0387\"; F61 (B9, Copyright): \"© International Labour Organization 2025 Attribution 4.0 International (CC BY 4.0)\"; F62 (B8, card snippet (Scribd unreachable: no URL found)): \"ILO Working Paper 140. May / 2025. XX Generative AI and Jobs. A Refined Global Index of Occupational Exposure. Authors / Pawel Gmyrek, Janine Berg, Karol KamiƄs...\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-36, LV-37, LV-40, LV-47", "archive_claims": [ "LV-36", "LV-37", "LV-40", "LV-47" ] }, { "lineage": "where it lives: the ILO's Observatory and later work", "field_claims": [ "F63", "F64" ], "received": "F63 (B10, 'AI impact on jobs'): \"ILO researchers developed a methodology to estimate the effects of generative AI on existing occupations.\"; F64 (B10, 'Spotlight'): \"Disruption without dividend? - How the digital divide and task differences split GenAI’s global impact ILO working paper 166 
 17 March 2026 
 Workers’ exposure to AI: What indicators tell us – and what they don’t Research brief 
 17 April 2026\"", "receiving_text": "—", "translation": "untranslated: composed as received beside LV-38, LV-45, LV-46", "archive_claims": [ "LV-38", "LV-45", "LV-46" ] }, { "lineage": "reception: commentary", "field_claims": [ "F65", "F66", "F67" ], "received": "F65 (B3, card snippet (LinkedIn unreachable: no URL found)): \"This International Labour Organization report on Generative AI and Jobs [May 2025] presents a comprehensive assessme...\"; F66 (B5, ¶1): \"Drawing on nearly 30,000 real-world tasks, more than 50,000 human assessments and expert validation it presents a refined global index of occupational exposure to generative AI.\"; F67 (B5, 'One in four jobs touched by GenAI' ¶1): \"Just 3.3% of jobs fall into the highest exposure category – those where most tasks could feasibly be handled by GenAI without human input.\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] }, { "lineage": "reception: the encyclopedia", "field_claims": [ "F72", "F73", "F74", "F75", "F76" ], "received": "F72 (B11, Wikipedia: titles 'Generative AI and Jobs: A Refined Global Index of Occupational Exposure', 'Global Index of Occupational Exposure to Generative AI', 'ILO Generative AI and Jobs index'): \"Wikipedia does not have an article with this exact name.\"; F73 (B11, Gender equality in the technology industry, §Statistics (cites WP140)): \"A [[United Nations]] report predicts that females will likely bear the brunt of future layoffs in technological fields.\"; F74 (B11, Technological unemployment, §Artificial intelligence (cites WP140 beside Yahoo Lifestyle)): \"In Australia 49% of larger firms and 37% of Small and Medium Enterprises have cut roles as a result of AI.\"; F75 (B11, Software engineering, §Employment, United States (cites WP140 beside an HP blog)): \"Furthermore, the ratio of women in many software fields has also been declining over the years as compared to other engineering fields.\"; F76 (B11, Programmer, §Market changes in the US (cites WP140 beside an HP blog)): \"enrollment in computer-related degrees and other STEM degrees (STEM attrition) 
 in the US has been dropping for years, especially for women,\"", "receiving_text": "—", "translation": "untranslated: composed as received (no archive claim in this lineage)", "archive_claims": [] } ], "receiving_texts": "equal-terms case: every field claim composed as received, beside the archive claims it stands with" }, "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 match over KO sentences)", "v0.9": { "surviving": 0, "of": 14, "positions": [ null, null, null, null, null, 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": "MMAMMMFMMMMMFMMMMAMMAAFFAAAMFF", "field_only_head": 0, "archive_only_tail": 0, "breach": false }, "max_similarity": [ 0.762, 0.237, 0.211, 0.179, 0.214, 0.175, 0.301, 0.195, 0.364, 0.387, 0.339, 0.154, 0.229, 0.325 ], "ko_sentence_1_max_similarity_to_KO_B": 0.762 }, "homonyms": { "names": [], "basis": "The census row carries no disambiguation field, and no card is a homonym: every card concerns the index, its brief, its makers' pages or commentary on it. 'Generative AI and Jobs' is also the head of the 2023 paper's title (card 6); the 2023 index is the entity's predecessor, a distinct entity, never a sense of the name.", "distinct_entities": [ { "name": "Generative AI and Jobs: A Global Analysis of Potential Effects on Job Quantity and Quality (Gmyrek, Berg and Bescond, ILO Working Paper 96, August 2023)", "relation": "predecessor: the first ILO index, which this one updates (F9); card 6 carries its title; Wikipedia's Artificial general intelligence cites it" }, { "name": "Generative AI and jobs: A 2025 update (ILO research brief, 20 May 2025)", "relation": "the companion brief that summarizes the working paper (card 2); carried as part of the entity's account, as the census does ('aka')" }, { "name": "ILO–NASK Index (nask.pl/index)", "relation": "NASK's presentation of the same index under its own name (card 4), with its dataset; the same entity, a name the census record does not list" }, { "name": "Generatywna sztuczna inteligencja a polski rynek pracy (NASK, 2025)", "relation": "a subsequent study on the Polish labour market in the same ILO–NASK series (the paper's Introduction announces it); distinct, not read" }, { "name": "How is generative AI reshaping gender inequalities at work? (ILO; card 7's title, also an ILO podcast episode)", "relation": "a separate ILO communication on gender; distinct" }, { "name": "Disruption without dividend? (ILO Working Paper 166, 17 March 2026) and Workers' exposure to AI: What indicators tell us – and what they don't (ILO research brief, 17 April 2026)", "relation": "later ILO works on the Observatory (F64); whether they revise this index is unchecked in the record (LV-46)" }, { "name": "ILO Policy Tracker on Digital Labour Platforms (census ilo-platform-policy-tracker)", "relation": "the record's neighbour on the Observatory (LV-45); its /non row, if built, at datasets/negative-of-the-negative/v2/rows/ilo-platform-policy-tracker.json (not present at this writing; not edited)" } ], "substitutes": { "T": [] }, "register": { "address_1": { "slug": "ilo-generative-ai-and-jobs-index-aio-20261029", "obs_id": "OBS-79a0cee7426a" } } }, "notes": [ "Census record (report; not fixed): its saved text for the paper (sources/ilo-genai-exposure-index--wp140.txt) is the raw HTML, the extractor's ' block (text_chars 594,096 of 594,630 bytes); the row extracts B9.txt from the saved HTML by the census's own rule, and all 35 of the record's evidence quotes are present in it.", "Census record, operator (report): the record lists eight authors with Balint Nafradi, as the ILO's publication and brief pages do (F7); the paper's own HTML title block lists seven, without him (F8). The record gives NASK as part of the expert group; the paper's Introduction gives the study as the ILO's Research department 'partnered with NASK' (F5), and NASK's page gives the report as prepared by the NASK Analysis and Research Office, the ILO having 'played an active role' (F6).", "Census record, aka (report): NASK's name for the index, 'ILO–NASK Index' (card 4, F4), is not among the record's names.", "Census record, stated_limits (report): the paper also states that 'all tasks are weighted equally' (footnote 18, F48), that Poland is assumed 'representative of the upper threshold of automation potential' (§2, F51), that the gradients 'merely suggest the general trends' (F47), and qualifies transformation with 'fewer workers' possibly needed (F41); none is in the record's stated_limits or findings. Omissions, not contradictions.", "Census record, version_and_time (report): the ISBN given (9789220421871) is the HTML edition's; the ILO's page lists four (print 9789220421840, PDF 9789220421857, EPUB 9789220421864, HTML 9789220421871).", "Capture finding (report; not fixed): CAP-1-04 locates the paper's 29.2% as the 2023 index's combined share of augmentation and 'the big unknown'; the same paragraph of the paper (§11 ¶4) also holds 16.2%, the 2023 'big unknown' share (F30), so both numbers of the composition's 'roughly 29% vs. 16%' stand in the paper as 2023 category shares, not as female and male shares. The finding is right that the figure as given is in no source; it names one of its two numbers. The PER read note's 'twenty-nine and sixteen occur in neither saved source' holds only of '29%' and '16%' as written.", "Capture PER read note (report): 'the Least Exposed Occupations bullet is likewise not in the saved text' holds of the occupations named; the paper states that exposure 'declines with tasks of physical nature' (§3.1, F34), which the bullet's 'physical and non-routine tasks' composes at a coarser grain.", "Capture finding CAP-1-05 (report): The People Space's card already sets the two counts in one sentence ('nearly 30,000 real-world tasks, more than 50,000 human assessments', F66), as two measures; the composition's join is its own.", "Wikipedia (observation): four articles cite the paper (Programmer; Software engineering; Technological unemployment; Gender equality in the technology industry) for claims on STEM enrolment, women in software, Australian firms' job cuts and women bearing future layoffs in technological fields; 'Australia', 'STEM' and 'layoff' do not occur in the paper's saved text (B9.txt). 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