{ "row": "stanford-canaries", "entity": "stanford-canaries", "address": "Canaries in the Coal Mine", "addresses": [ { "address": "Canaries in the Coal Mine", "slug": "canaries-in-the-coal-mine-aio-20261029", "addr_id": "ADDR-6545749d8321", "obs_id": "OBS-8527c06bd274", "axn": "AXN:0AEA.CAPTURE.šŸŒ†šŸ•“šŸŒ’šŸ›ļøšŸƒā—€ļø" } ], "epoch": "2026-10-29", "type": "C (public entity on which the archive bears: a working paper and monthly dashboard of observed employment outcomes; an entity of the Labor Vector meta-aggregator; at the bare name, a homonym, the idiom)", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "status": { "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, Ļ = extends, read from the Labor Vector census record, the capture record and the deposit that bears on the entity's class (#716); archive selected by D/R/O (no D, R or O candidate from the string pass: no archive text names the dashboard, the paper or its authors; every archive sentence matching the four class patterns, 8 in 6 texts, read; 1 deposit admitted at O-class with 2 claims; hop of 29 read and rejected), with the census record (35 claims) and the Capture Registry's findings, reading, analysis, query note, PER note and PER-dim reading notes (13 claims) ledgered as the archive's records of the entity; the field B by three origins (1 card concerning the entity; 9 cards of the homonym, the idiom, recorded with their sense and not ledgered; 1 own page; Wikipedia, no article, three citing articles); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta", "ledger": "unaudited (Ā§3.8: one extractor per ledger); field quotes 56/56 verified by script against the saved page text, the PDF's text and the Wikipedia summary; archive quotes 50/50 verified against the registry, census.json, questions.json and the deposit text", "frozen": false, "breaches": [] }, "spec": "EA-NEGONT-02 v0.9, #1690", "objects": { "T": { "path": "datasets/negative-of-the-negative/v2/intake/stanford-canaries-20261029/T1-canaries-in-the-coal-mine-aio-20261029.txt", "sha256": "b4e101353182a340a196ca3d055920f5f6f4abb3ad879b7eaa8b32ac891d3731", "words": 225, "cards": 10, "words_note": "one transcript at one address (225 words in the answer body with its closer, links reduced to their text, markers and the card rail excluded; 10 cards, card 1 the entity's, cards 2ā€“10 the homonym's)", "claims": [ "T1-1 definition (the idiom): 'A \"canary in a coal mine\" is an early warning sign or indicator of hidden danger, trouble, or failure.' (two markers)", "T1-2 toxic gas detection: 'In the late 19th and 20th centuries, coal miners brought caged canaries into underground tunnels.' (two markers)", "T1-3 physiology: 'Canaries have a rapid breathing rate and high metabolism, making them much more sensitive to lethal, odorless gases like carbon monoxide and methane than humans.' (two markers)", "T1-4 the warning signal: 'As long as the bird sang and acted normally, the air was safe. If the canary grew distressed, fell silent, or collapsed, it signaled that poisonous gas was building up and miners needed to escape.' (three markers)", "T1-5 replacement: 'The practice continued until electronic gas detectors replaced live birds in the late 20th century (around 1996).' (two markers)", "T1-6 modern idiom: 'Today, the phrase describes anything that breaks down or shows negative changes first during a larger crisis' (two markers)", "T1-7 economics: 'Declining hotel occupancy or airport traffic used as an early sign of an incoming economic downturn.' (one marker)", "T1-8 environment: 'Melting glaciers, dying coral reefs, or disappearing insects used as early warnings for climate change and ecosystem stress.' (two markers)", "T1-9 technology & labor (the entity): 'Studies such as the Stanford Digital Economy Lab report tracking entry-level or young worker employment shifts as early indicators of artificial intelligence impacts on the job market.' (the Lab's name an inline link; one marker)", "T1-10 closer: 'Would you like to explore how this idiom is applied to a specific industry, environmental issue, or economic trend?'" ], "register": { "register": "data/EA-WG-CAPTURES-01.json", "slug": "canaries-in-the-coal-mine-aio-20261029", "obs_id": "OBS-8527c06bd274" }, "transcripts": [ { "slug": "canaries-in-the-coal-mine-aio-20261029", "address": "Canaries in the Coal Mine", "addr_id": "ADDR-6545749d8321", "obs_id": "OBS-8527c06bd274", "axn": "AXN:0AEA.CAPTURE.šŸŒ†šŸ•“šŸŒ’šŸ›ļøšŸƒā—€ļø", "date": "2026-10-29", "surface": "Google AI Overview (expanded)", "auth": "signed out, incognito", "sha256": "b4e101353182a340a196ca3d055920f5f6f4abb3ad879b7eaa8b32ac891d3731", "sha256_of": "the registry's 'transcript' string (utf-8)", "sha256_raw": "7420a185b17c78cd794a524ac8920556649cc8ca6192b140c50b62099f36a7bd", "words": 225, "cards": 10, "card_rail": [ "1. Stanford Digital Economy Lab ā€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ...", "2. The Gale Review ā€” The historic roots and modern connotations of \"Canary in the Coal Mine\"", "3. RedditĀ·r/AskHistorians ā€” Why were canaries used in coal mines? : r/AskHistorians - Reddit", "4. YouTubeĀ·iswearenglish ā€” Canary in the Coalmine Definition (video, 5:34)", "5. YouTubeĀ·Beamish Museum ā€” Why were canaries used down the mine? (video, 1m)", "6. Cambridge Dictionary ā€” Meaning of canary in a coalmine in English - Cambridge Dictionary", "7. Government of Alberta ā€” Canaries in the Coal Mine - Coal - Alberta's Energy Heritage", "8. YouTubeĀ·Tutor Nick P ā€” Canary in a Coalmine - Idioms (770) Origin - English Tutor Nick P (video, 2:04)", "9. YouTubeĀ·Smartest Year Ever ā€” Canaries in Coal Mines šŸ¤ #dailyfacts (video, 1:09)", "10. FacebookĀ·Amazing Time Capsule ā€” How Canaries Saved the Lives of Miners | Amazing Time Capsule (video, 1m)" ], "claims": [ "T1-1 definition (the idiom): 'A \"canary in a coal mine\" is an early warning sign or indicator of hidden danger, trouble, or failure.' (two markers)", "T1-2 toxic gas detection: 'In the late 19th and 20th centuries, coal miners brought caged canaries into underground tunnels.' (two markers)", "T1-3 physiology: 'Canaries have a rapid breathing rate and high metabolism, making them much more sensitive to lethal, odorless gases like carbon monoxide and methane than humans.' (two markers)", "T1-4 the warning signal: 'As long as the bird sang and acted normally, the air was safe. If the canary grew distressed, fell silent, or collapsed, it signaled that poisonous gas was building up and miners needed to escape.' (three markers)", "T1-5 replacement: 'The practice continued until electronic gas detectors replaced live birds in the late 20th century (around 1996).' (two markers)", "T1-6 modern idiom: 'Today, the phrase describes anything that breaks down or shows negative changes first during a larger crisis' (two markers)", "T1-7 economics: 'Declining hotel occupancy or airport traffic used as an early sign of an incoming economic downturn.' (one marker)", "T1-8 environment: 'Melting glaciers, dying coral reefs, or disappearing insects used as early warnings for climate change and ecosystem stress.' (two markers)", "T1-9 technology & labor (the entity): 'Studies such as the Stanford Digital Economy Lab report tracking entry-level or young worker employment shifts as early indicators of artificial intelligence impacts on the job market.' (the Lab's name an inline link; one marker)", "T1-10 closer: 'Would you like to explore how this idiom is applied to a specific industry, environmental issue, or economic trend?'" ], "markers": "Inline links are google.com/goto tokens, opaque; the paste is the Overview expanded (operator's default of 2026-10-01; the 'AI Mode Conversation' view is the expanded Overview, ruling of 2026-10-18); signed out, incognito (operator's default of 2026-10-14). One turn. The 'You said' line carried the query twice, run together, and the registry gives it once. The markers cannot be resolved to cards; the Lab's name in T1-9 is an inline link of its own.", "path": "datasets/negative-of-the-negative/v2/intake/stanford-canaries-20261029/T1-canaries-in-the-coal-mine-aio-20261029.txt" } ] }, "KO": { "title": "Canaries Dashboard (Canaries in the Coal Mine?)", "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, ordered by the entity's dependency", "status": "composed 2026-10-29 under v0.9 on the operator's ruling ('as against their wikipedia articles if they exist, their own pages, etc.'); not frozen; ledgers unaudited", "sentences": [ { "n": 1, "para": 0, "sense": "what it is", "text": "The Canaries Dashboard is an observed-employment-outcome resource on the distribution of gains and losses, a working paper and monthly-updated dashboard among the Stanford Digital Economy Lab's AI Economic Indicators (Labor Vector census record), which in its own words summarizes, in collaboration with ADP Research, \"live labor market trends for a sample workers in firms using ADP payroll services\" (the dashboard).", "claims": [ "LV-01", "LV-03", "LV-05", "LV-06", "F32" ] }, { "n": 2, "para": 0, "sense": "what it is", "text": "The dashboard updates and extends the working paper \"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence\" by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen (the dashboard; the paper's page), first released in August 2025 and revised on 12 August 2026 (the paper; census record), and the census lists the paper's title and the bare phrase among the entity's names (census record).", "claims": [ "F33", "F1", "F2", "F3", "F21", "LV-26", "LV-02" ] }, { "n": 3, "para": 1, "sense": "the name", "text": "The name is the idiom put to work: the authors write that generative AI's \"proficiency with codified knowledge work\" suggests \"there may be ā€œcanaries in the coal mineā€ which will be affected first\" (the paper), and the dashboard calls its granular, timely results \"an early signal of changes to the labor market, a canary in the coal mine\", complementing rather than substituting for analyses of nationally representative data (the dashboard).", "claims": [ "F18", "F35" ] }, { "n": 4, "para": 1, "sense": "the name", "text": "On the dashboard's premise, \"specific segments of the labor market can provide a leading indicator of employment trends for the broader economy\" (the dashboard), and Bharat Chandar's page describes the work as \"tracking ā€œcanaries in the coal mineā€ for entry-level employment changes in jobs exposed to AI\" (the paper's page).", "claims": [ "F34", "F14" ] }, { "n": 5, "para": 1, "sense": "the name", "text": "The bare phrase is therefore a homonym of the study by construction, the paper's question mark marking its title as a use of the idiom, and the phrase is an idiom before it is a title (Capture Registry, 2026-10-29); the name is also the idiom's (Capture Registry), and Wikipedia resolves the idiom to its article \"Sentinel species\", section \"Canaries\", not to the study (Wikipedia).", "claims": [ "CAP-1-05", "CAP-1-06", "CAP-1-10", "F53" ] }, { "n": 6, "para": 2, "sense": "operator", "text": "Its operator is the Stanford Digital Economy Lab with ADP Research, the paper by Brynjolfsson, Chandar and Chen (census record), and the dashboard asks to be cited as the Stanford Digital Economy Lab and ADP Research's \"AI Economic Indicators: Canaries Dashboard\" (the dashboard).", "claims": [ "LV-04", "F36" ] }, { "n": 7, "para": 3, "sense": "the data", "text": "The paper uses \"high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026\" to document six facts about the labor market after the adoption of generative AI, and balances \"timeliness, worker granularity, and administrative verifiability\"; ADP serves firms employing over 26 million U.S. workers, and the analysis panel holds between 3.5 and 5 million employees a month (the paper).", "claims": [ "F4", "F26", "F27" ] }, { "n": 8, "para": 3, "sense": "the data", "text": "The dashboard adopts the paper's balanced sample, firms with five years of employment history in ADP, rolled forward monthly and limited to workers matched to an occupation code: at launch 25,000 firms, 4.6 million matched workers in November 2022 and over 730 occupations (the dashboard; census record), employment indexed from November 2022 and descriptive indices beside event-study regressions (census record).", "claims": [ "F37", "F38", "LV-07", "LV-14", "LV-12", "LV-13" ] }, { "n": 9, "para": 3, "sense": "the data", "text": "Whose labor it observes the census gives as the employment of these workers grouped by age and by their occupation's AI exposure, the labor supplied by \"workers in firms using ADP payroll services (the payroll records)\" (census record).", "claims": [ "LV-08", "LV-09" ] }, { "n": 10, "para": 4, "sense": "exposure and usage", "text": "Exposure is matched from \"Eloundou et al. (2024) and the Anthropic Economic Index\" (the dashboard), so the dashboard is not itself a usage measure (census record) and takes from the index its augmentation and automation ratios by occupation (census record); in the index's terms as the dashboard gives them, \"users fully delegate tasks\" under automation, while augmentation \"features collaboration between humans and an AI system\" (the dashboard).", "claims": [ "F39", "LV-27", "LV-29", "F40" ] }, { "n": 11, "para": 5, "sense": "the six facts", "text": "The paper finds \"no evidence of widespread, economy-wide job displacement\" (the paper; census record).", "claims": [ "F5", "LV-15", "LV-28" ] }, { "n": 12, "para": 5, "sense": "the six facts", "text": "Its headline is that employment of workers aged 22 to 25 in AI-exposed occupations \"now stands 19% below where it would be had it kept pace with that of their less-exposed peers\", experienced workers showing \"no comparable gap\" (the paper; census record); in levels, that age group fell about 11% in the two most exposed quintiles from November 2022 to June 2026 and grew about 10% in the three least exposed (the paper).", "claims": [ "F6", "LV-16", "F19" ] }, { "n": 13, "para": 5, "sense": "the six facts", "text": "The divergence \"has widened steadily since we first documented it in August 2025\", operates \"primarily through reduced hiring of young workers rather than increased separations\", and adjusts \"through employment rather than base compensation\" (the paper).", "claims": [ "F7", "F8", "F10" ] }, { "n": 14, "para": 5, "sense": "who directs, who gains", "text": "The census reads this as a record of costs, the shortfall of young workers' employment in exposed occupations with adjustment through employment, and records who directs the hiring as not recorded and who receives the gains as not recorded, which the evidence cannot settle (census record).", "claims": [ "LV-10", "LV-11", "LV-23" ] }, { "n": 15, "para": 6, "sense": "automation against augmentation", "text": "Declines concentrate \"in occupations where AI usage primarily substitutes for human tasks\", while where usage complements workers employment is \"flat or rising, especially for experienced workers\" (the paper; census record); on the dashboard the complementarity share shows no clear monotonic relationship with employment trends, while the automation ratio \"shows a clear correlation with employment trends\" (the dashboard; census record).", "claims": [ "F9", "LV-17", "F41", "LV-21" ] }, { "n": 16, "para": 6, "sense": "automation against augmentation", "text": "The dashboard draws from this that \"the character of AI usage could shape the labor market effects of AI\" (the dashboard), and the census reads the same grouping, borrowed from the Anthropic Economic Index, as \"where a usage measure of who directs the task meets an observed employment outcome\", which the resource itself reads as correlation (census record).", "claims": [ "F42", "LV-24" ] }, { "n": 17, "para": 7, "sense": "the provenance channel", "text": "The archive's Provenance Erasure Rate paper names the channels AI-economics research measures, labor displacement among them, as frameworks that \"measure which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates\", and sets beside them a channel where human labor remains and the value tied to provenance is extracted: \"This is not displacement; it is extraction without attribution\" (#716).", "claims": [ "O716-01" ] }, { "n": 18, "para": 7, "sense": "the provenance channel", "text": "It names that fourth channel compositional authority transfer, \"the rate at which AI synthesis captures value from human authors by stripping the attribution that would otherwise carry economic weight\" (#716).", "claims": [ "O716-02" ] }, { "n": 19, "para": 8, "sense": "mechanisms", "text": "The paper offers \"suggestive evidence about mechanisms\", declines for young workers in occupations that involve codified knowledge, and as \"One possibility\" that AI substitutes for codified knowledge while complementing the tacit knowledge of senior employees; the entry-level hiring slowdown \"provides early suggestions that generative AI may be affecting the career ladder\" (the paper).", "claims": [ "F22", "F23", "F24" ] }, { "n": 20, "para": 8, "sense": "gender and occupations", "text": "Women face greater AI exposure on average (the paper); early-career women in the most-exposed quintile have seen employment contract 4.5% a year since November 2022 against 2.5% for men, a difference the dashboard finds \"driven primarily by occupational composition\" (the dashboard, updated 22 July 2026; census record), and early-career software developers and customer service workers show substantial declines while home health aides grow for the youngest (the dashboard).", "claims": [ "F25", "F43", "F44", "LV-18", "F45" ] }, { "n": 21, "para": 9, "sense": "robustness", "text": "The divergence persists when excluding technology firms and computer occupations, when controlling for interest-rate exposure and remote work, and across alternative exposure measures, and is robust to allowing firm entry and exit (the paper); with the broadest set of controls, declines in exposed occupations become statistically significant in 2024, the earlier declines \"likely driven by non-AI factors\" (the dashboard, from the February 2026 update).", "claims": [ "F11", "F29", "F46" ] }, { "n": 22, "para": 9, "sense": "the stated limit", "text": "The authors interpret the facts \"as early, descriptive indicatorsā€”canaries in the coal mineā€”rather than causal estimates\" and caution that the work \"does not estimate a causal impact of AI\"; the patterns attenuate with education controls, show some divergence predating generative AI and are more pronounced in the ADP sample than in national survey benchmarks, and the authors make no predictions (the paper).", "claims": [ "F13", "F30", "F12", "F31" ] }, { "n": 23, "para": 9, "sense": "the stated limit", "text": "The dashboard \"measures the correlation between occupational exposure measures and employment trends in our sample, rather than a causal link between the two\" (the dashboard), and the census carries the limit with what the evidence cannot settle: \"whether AI exposure causes the employment change\" (census record).", "claims": [ "F47", "LV-19", "LV-22" ] }, { "n": 24, "para": 9, "sense": "the sample's limits", "text": "Its sample is \"large, but not necessarily representative\" and \"represents neither the entire U.S. labor market nor the universe of firms using ADP payroll services\", the balanced sample \"shuts down the margins of firm entry and exit\" (the dashboard; census record), and there may be selection in which firms use ADP (the paper).", "claims": [ "F48", "F49", "LV-20", "F28" ] }, { "n": 25, "para": 10, "sense": "versions and lifecycle", "text": "Earlier versions headlined regression estimates, a 13% relative decline at July 2025 data and 16% at September 2025 data, and the August 2026 revision emphasizes the descriptive divergence, 15% at the July 2025 vintage and 19% as of June 2026 (the paper), after a February 2026 update addressing the response to the paper and on a larger set of data (the paper's page).", "claims": [ "F20", "F15", "F16" ] }, { "n": 26, "para": 10, "sense": "versions and lifecycle", "text": "The dashboard was \"Last updated October 21, 2026\" and is updated monthly, with \"an expanded and reweighted sample\" announced (the dashboard; census record); earlier versions of the paper are available by email (the paper's page; census record), its license is not stated in the saved text, and the census left the PDF, the chart data, the authors' notes and the license unchecked (census record).", "claims": [ "F50", "F51", "LV-25", "F17", "LV-35", "LV-34", "LV-33" ] }, { "n": 27, "para": 11, "sense": "who directs, who gains", "text": "The census asks of it its organizing question, \"Who supplies the labor, who directs it, and who receives its gains ā€” and what kind of evidence does this resource bring to that question?\", and places it in two worked questions, as observed employment change set against exposure estimates and as costs observed where no record measures the distribution of productivity gains (census record).", "claims": [ "LV-30", "LV-31", "LV-32" ] }, { "n": 28, "para": 12, "sense": "the composition at the name", "text": "At the bare name on 29 October 2026, Google's AI Overview composed the idiom and carried the study, the Lab's Canaries Dashboard with ADP Research, in one bullet, \"at the grain of its subject (young workers' employment as an early indicator)\": the Lab named, no author, no identifier, the paper's page cited as card 1, with \"early indicators\" the record's own frame and no causal or displacement claim made (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-01", "CAP-1-02", "CAP-1-11", "CAP-1-12" ] }, { "n": 29, "para": 12, "sense": "the composition at the name", "text": "The finding, the dashboard and its limits are absent; \"An idiom outranks the study named after it, which survives as an example of the idiom's use\"; the generic sense is the target's own frame turned outward and is not carried to devalue the study; the registry reads that the record would be reached at its title with its question mark or at the Lab's name; and no card cites the census record (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-03", "CAP-1-04", "CAP-1-07", "CAP-1-13", "CAP-1-08", "CAP-1-09" ] }, { "n": 30, "para": 13, "sense": "reception", "text": "Wikipedia has no article on the paper, the dashboard or the Lab; three articles cite the paper, \"AI takeover\" with the August 2025 version's 13% relative decline, \"Artificial intelligence\" with decreasing employment rates for early-career workers, and \"Silicon Valley\" with the 19% gap, which it sets against \"older, less exposed workers\" where the paper compares less-exposed peers (Wikipedia; the paper).", "claims": [ "F52", "F54", "F55", "F56", "F6" ] } ], "joins": [ { "act": "order of senses", "sentences": "1ā€“30", "claims": [], "note": "equal case (Ā§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 (Ā¶0) precedes the name (Ā¶1), since the name is the paper's title and the dashboard's self-description; the operator (Ā¶2) and the data (Ā¶3) precede the exposure measures (Ā¶4), which group the data; the six facts (Ā¶5) stand on the data and are read at once by the census's cost reading; automation against augmentation (Ā¶6) is the fifth fact and carries the census's reading of direction; the provenance channel (#716, Ā¶7) stands beside the class's channels as the archive names them; mechanisms, gender and occupations (Ā¶8) qualify the facts; robustness and the limits (Ā¶9) qualify everything above; versions (Ā¶10), the census's question (Ā¶11), the composition at the name (Ā¶12) and the encyclopedia (Ā¶13) stand last as relations of the entity already composed." }, { "act": "holding together (homonym)", "sentences": [ 3, 4, 5 ], "claims": [ "F18", "F35", "F34", "F14", "CAP-1-05", "CAP-1-06", "CAP-1-10", "F53" ], "note": "the idiom composed as the paper's and the dashboard's own figure for what they observe, beside the registry's reading that the bare phrase is the idiom's before it is the title's; the idiom kept a distinct entity (the homonym), never a sense of the entity's name." }, { "act": "holding together (direction)", "sentences": [ 14, 16, 27 ], "claims": [ "LV-10", "LV-11", "LV-23", "LV-24", "F42", "LV-30" ], "note": "the census's reading that who directs and who gains is not recorded, beside the dashboard's hypothesis that the character of usage could shape effects and the census's reading of the automation ratio as a usage measure of who directs the task. Each at its own grade; the composer does not rule that the ratio measures direction (open block)." }, { "act": "holding together (the provenance channel)", "sentences": [ 17, 18 ], "claims": [ "O716-01", "O716-02" ], "note": "#716's channel set beside the class, labor displacement research, on #716's own terms: an addition to the channels the class measures, not a reading of the entity's findings." }, { "act": "holding together (firm entry and exit)", "sentences": [ 21, 24 ], "claims": [ "F29", "F49" ], "note": "the paper's robustness to allowing firm entry and exit composed beside the dashboard's statement that its balanced sample shuts those margins: two instruments of one work, each with its source (P_coh)." }, { "act": "holding together (reception against the primary)", "sentences": [ 30 ], "claims": [ "F56", "F6", "F54", "F20" ], "note": "the encyclopedia's sentences composed with the paper's own statements they report: the 13% of 'AI takeover' is the earlier headline the paper now revises (F20); 'Silicon Valley''s 'older, less exposed workers' is marked contradicts_primary against F6." } ], "plan": { "order": [ "LV-01", "LV-03", "LV-05", "LV-06", "F32", "F33", "F1", "F2", "F3", "F21", "LV-26", "LV-02", "F18", "F35", "F34", "F14", "CAP-1-05", "CAP-1-06", "CAP-1-10", "F53", "LV-04", "F36", "F4", "F26", "F27", "F37", "F38", "LV-07", "LV-14", "LV-12", "LV-13", "LV-08", "LV-09", "F39", "LV-27", "LV-29", "F40", "F5", "LV-15", "LV-28", "F6", "LV-16", "F19", "F7", "F8", "F10", "LV-10", "LV-11", "LV-23", "F9", "LV-17", "F41", "LV-21", "F42", "LV-24", "O716-01", "O716-02", "F22", "F23", "F24", "F25", "F43", "F44", "LV-18", "F45", "F11", "F29", "F46", "F13", "F30", "F12", "F31", "F47", "LV-19", "LV-22", "F48", "F49", "LV-20", "F28", "F20", "F15", "F16", "F50", "F51", "LV-25", "F17", "LV-35", "LV-34", "LV-33", "LV-30", "LV-31", "LV-32", "CAP-1-01", "CAP-1-02", "CAP-1-11", "CAP-1-12", "CAP-1-03", "CAP-1-04", "CAP-1-07", "CAP-1-13", "CAP-1-08", "CAP-1-09", "F52", "F54", "F55", "F56" ], "senses": { "what it is": [ 1, 2 ], "the name": [ 3, 4, 5 ], "operator": [ 6 ], "the data": [ 7, 8, 9 ], "exposure and usage": [ 10 ], "the six facts": [ 11, 12, 13 ], "who directs, who gains": [ 14, 27 ], "automation against augmentation": [ 15, 16 ], "the provenance channel": [ 17, 18 ], "mechanisms": [ 19 ], "gender and occupations": [ 20 ], "robustness": [ 21 ], "the stated limit": [ 22, 23 ], "the sample's limits": [ 24 ], "versions and lifecycle": [ 25, 26 ], "the composition at the name": [ 28, 29 ], "reception": [ 30 ] }, "case_by_sense": { "equal (one claim space; nothing translated)": [ "what it is", "the name", "operator", "the data", "exposure and usage", "the six facts", "who directs, who gains", "automation against augmentation", "the provenance channel", "mechanisms", "gender and occupations", "robustness", "the stated limit", "the sample's limits", "versions and lifecycle", "the composition at the name", "reception" ] }, "carried": { "archive": 50, "field": 56, "not_composed": {} }, "conflict_log": [ { "rule": "D_pres vs channel", "result": "no cut: the KO carries every field and archive claim; the compression's bound met with no channel log" }, { "rule": "P_coh", "result": "F49 (the dashboard: the balanced sample 'shuts down the margins of firm entry and exit') beside F29 (the paper: the patterns 'robust to allowing for firm entry and exit') (sentences 24 and 21); F56 (Silicon Valley: 'older, less exposed workers') marked contradicts_primary against F6 ('less-exposed peers'), both composed with their sources (sentence 30); F20 (13% and 16%, then 15% and 19%) beside F54 (AI takeover's 13%) (sentences 25, 30): each composed with its source" }, { "rule": "N_c", "result": "no claim lowered by standing; 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Six Facts about the Recent Employment Effects of Artificial Intelligence\" is a working paper by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen of the Stanford Digital Economy Lab, first released in August 2025 and revised on 12 August 2026 (the paper's page; the paper).", "claims": [ "F1", "F2", "F3", "F21" ] }, { "n": 2, "para": 0, "text": "Using \"high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026\", it documents six facts about the labor market after generative AI, addressing \"timeliness, worker granularity, and administrative verifiability\" on a balanced panel of 3.5 to 5 million employees a month (the paper).", "claims": [ "F4", "F26", "F27" ] }, { "n": 3, "para": 1, "text": "It finds no evidence of widespread, economy-wide job displacement, but employment of workers aged 22 to 25 in AI-exposed occupations stands 19% below where it would be had it kept pace with less-exposed peers, with no comparable gap for experienced workers; in levels, about 11% down against about 10% up (the paper).", "claims": [ "F5", "F6", "F19" ] }, { "n": 4, "para": 1, "text": "The divergence has widened since August 2025, runs through reduced hiring rather than separations, and adjusts through employment rather than base compensation (the paper).", "claims": [ "F7", "F8", "F10" ] }, { "n": 5, "para": 1, "text": "Declines concentrate where AI usage substitutes for human tasks and employment is flat or rising where it complements workers (the paper); on the dashboard the automation ratio correlates with employment trends where the complementarity share does not clearly, and \"the character of AI usage could shape the labor market effects of AI\" (the dashboard).", "claims": [ "F9", "F41", "F42" ] }, { "n": 6, "para": 2, "text": "The paper reads suggestive evidence of mechanisms in codified knowledge, offers as one possibility that AI substitutes for codified and complements tacit knowledge, and sees early suggestions of an effect on the career ladder (the paper).", "claims": [ "F22", "F23", "F24" ] }, { "n": 7, "para": 2, "text": "Women face greater exposure, and early-career women in the most-exposed quintile contract 4.5% a year against 2.5% for men, a difference driven primarily by occupational composition; early-career software developers and customer service workers decline while young home health aides grow (the paper; the dashboard).", "claims": [ "F25", "F43", "F44", "F45" ] }, { "n": 8, "para": 3, "text": "The divergence survives exclusions of technology firms and computer occupations, interest-rate and remote-work controls and other exposure measures, and allowing for firm entry and exit; with the broadest controls the declines become significant in 2024 (the paper; the dashboard).", "claims": [ "F11", "F29", "F46" ] }, { "n": 9, "para": 3, "text": "The authors read their facts \"as early, descriptive indicatorsā€”canaries in the coal mineā€”rather than causal estimates\", note attenuation under education controls, earlier divergence and stronger patterns in ADP than in national benchmarks, make no predictions, and caution that the work \"does not estimate a causal impact of AI\" (the paper).", "claims": [ "F13", "F12", "F31", "F30" ] }, { "n": 10, "para": 4, "text": "The title takes up the paper's premise that generative AI's proficiency with codified knowledge work suggests \"canaries in the coal mine\" affected first, and Chandar's page describes the work as tracking them for entry-level employment changes (the paper; the paper's page).", "claims": [ "F18", "F14" ] }, { "n": 11, "para": 5, "text": "The Canaries Dashboard, \"AI Economic Indicators: Canaries Dashboard\" of the Stanford Digital Economy Lab and ADP Research, updates and extends the paper with live trends for a sample of workers in ADP firms, on the premise that specific segments of the labor market can lead the broader economy, and calls itself \"an early signal of changes to the labor market, a canary in the coal mine\" (the dashboard).", "claims": [ "F32", "F33", "F36", "F34", "F35" ] }, { "n": 12, "para": 5, "text": "It uses a balanced sample of firms with five years of ADP history rolled forward monthly, at launch 25,000 firms and 4.6 million matched workers across over 730 occupations, and matches exposure from Eloundou et al. 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Six Facts about the Recent Employment Effects of Artificial Intelligence\" is a working paper by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen of the Stanford Digital Economy Lab, first released in August 2025 and revised on 12 August 2026 (the paper's page; the paper). Using \"high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026\", it documents six facts about the labor market after generative AI, addressing \"timeliness, worker granularity, and administrative verifiability\" on a balanced panel of 3.5 to 5 million employees a month (the paper). It finds no evidence of widespread, economy-wide job displacement, but employment of workers aged 22 to 25 in AI-exposed occupations stands 19% below where it would be had it kept pace with less-exposed peers, with no comparable gap for experienced workers; in levels, about 11% down against about 10% up (the paper). The divergence has widened since August 2025, runs through reduced hiring rather than separations, and adjusts through employment rather than base compensation (the paper). Declines concentrate where AI usage substitutes for human tasks and employment is flat or rising where it complements workers (the paper); on the dashboard the automation ratio correlates with employment trends where the complementarity share does not clearly, and \"the character of AI usage could shape the labor market effects of AI\" (the dashboard). The paper reads suggestive evidence of mechanisms in codified knowledge, offers as one possibility that AI substitutes for codified and complements tacit knowledge, and sees early suggestions of an effect on the career ladder (the paper). Women face greater exposure, and early-career women in the most-exposed quintile contract 4.5% a year against 2.5% for men, a difference driven primarily by occupational composition; early-career software developers and customer service workers decline while young home health aides grow (the paper; the dashboard). The divergence survives exclusions of technology firms and computer occupations, interest-rate and remote-work controls and other exposure measures, and allowing for firm entry and exit; with the broadest controls the declines become significant in 2024 (the paper; the dashboard). The authors read their facts \"as early, descriptive indicatorsā€”canaries in the coal mineā€”rather than causal estimates\", note attenuation under education controls, earlier divergence and stronger patterns in ADP than in national benchmarks, make no predictions, and caution that the work \"does not estimate a causal impact of AI\" (the paper). The title takes up the paper's premise that generative AI's proficiency with codified knowledge work suggests \"canaries in the coal mine\" affected first, and Chandar's page describes the work as tracking them for entry-level employment changes (the paper; the paper's page). The Canaries Dashboard, \"AI Economic Indicators: Canaries Dashboard\" of the Stanford Digital Economy Lab and ADP Research, updates and extends the paper with live trends for a sample of workers in ADP firms, on the premise that specific segments of the labor market can lead the broader economy, and calls itself \"an early signal of changes to the labor market, a canary in the coal mine\" (the dashboard). It uses a balanced sample of firms with five years of ADP history rolled forward monthly, at launch 25,000 firms and 4.6 million matched workers across over 730 occupations, and matches exposure from Eloundou et al. (2024) and the Anthropic Economic Index, whose terms set full delegation (automation) against collaboration (augmentation) (the dashboard). It measures correlation and not a causal link, on a sample representative neither of the U.S. labor market nor of all ADP firms and closed to firm entry and exit, with possible selection in which firms use ADP (the dashboard; the paper). Updated monthly, last on 21 October 2026, it announces an expanded and reweighted sample; the paper's earlier versions headlined regression estimates of 13% and 16% before the descriptive 19%, a February 2026 update answered the response, the August revision used a larger set of data, and earlier versions are available by email (the dashboard; the paper; the paper's page). Wikipedia has no article on the paper or the dashboard and resolves the idiom to \"Sentinel species\"; \"AI takeover\", \"Artificial intelligence\" and \"Silicon Valley\" cite the paper, the first with the 2025 figure of 13%, the last against \"older, less exposed workers\" (Wikipedia).", "note": "L_F(B), the field composed in the field's ontology (v0.9 Ā§0.4), realized as KO_B; its rail is P_B's, by lineage." }, "L_BA": { "words": 1713, "text": "The Canaries Dashboard is an observed-employment-outcome resource on the distribution of gains and losses, a working paper and monthly-updated dashboard among the Stanford Digital Economy Lab's AI Economic Indicators (Labor Vector census record), which in its own words summarizes, in collaboration with ADP Research, \"live labor market trends for a sample workers in firms using ADP payroll services\" (the dashboard). The dashboard updates and extends the working paper \"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence\" by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen (the dashboard; the paper's page), first released in August 2025 and revised on 12 August 2026 (the paper; census record), and the census lists the paper's title and the bare phrase among the entity's names (census record). The name is the idiom put to work: the authors write that generative AI's \"proficiency with codified knowledge work\" suggests \"there may be ā€œcanaries in the coal mineā€ which will be affected first\" (the paper), and the dashboard calls its granular, timely results \"an early signal of changes to the labor market, a canary in the coal mine\", complementing rather than substituting for analyses of nationally representative data (the dashboard). On the dashboard's premise, \"specific segments of the labor market can provide a leading indicator of employment trends for the broader economy\" (the dashboard), and Bharat Chandar's page describes the work as \"tracking ā€œcanaries in the coal mineā€ for entry-level employment changes in jobs exposed to AI\" (the paper's page). The bare phrase is therefore a homonym of the study by construction, the paper's question mark marking its title as a use of the idiom, and the phrase is an idiom before it is a title (Capture Registry, 2026-10-29); the name is also the idiom's (Capture Registry), and Wikipedia resolves the idiom to its article \"Sentinel species\", section \"Canaries\", not to the study (Wikipedia). Its operator is the Stanford Digital Economy Lab with ADP Research, the paper by Brynjolfsson, Chandar and Chen (census record), and the dashboard asks to be cited as the Stanford Digital Economy Lab and ADP Research's \"AI Economic Indicators: Canaries Dashboard\" (the dashboard). The paper uses \"high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026\" to document six facts about the labor market after the adoption of generative AI, and balances \"timeliness, worker granularity, and administrative verifiability\"; ADP serves firms employing over 26 million U.S. workers, and the analysis panel holds between 3.5 and 5 million employees a month (the paper). The dashboard adopts the paper's balanced sample, firms with five years of employment history in ADP, rolled forward monthly and limited to workers matched to an occupation code: at launch 25,000 firms, 4.6 million matched workers in November 2022 and over 730 occupations (the dashboard; census record), employment indexed from November 2022 and descriptive indices beside event-study regressions (census record). Whose labor it observes the census gives as the employment of these workers grouped by age and by their occupation's AI exposure, the labor supplied by \"workers in firms using ADP payroll services (the payroll records)\" (census record). Exposure is matched from \"Eloundou et al. (2024) and the Anthropic Economic Index\" (the dashboard), so the dashboard is not itself a usage measure (census record) and takes from the index its augmentation and automation ratios by occupation (census record); in the index's terms as the dashboard gives them, \"users fully delegate tasks\" under automation, while augmentation \"features collaboration between humans and an AI system\" (the dashboard). The paper finds \"no evidence of widespread, economy-wide job displacement\" (the paper; census record). Its headline is that employment of workers aged 22 to 25 in AI-exposed occupations \"now stands 19% below where it would be had it kept pace with that of their less-exposed peers\", experienced workers showing \"no comparable gap\" (the paper; census record); in levels, that age group fell about 11% in the two most exposed quintiles from November 2022 to June 2026 and grew about 10% in the three least exposed (the paper). The divergence \"has widened steadily since we first documented it in August 2025\", operates \"primarily through reduced hiring of young workers rather than increased separations\", and adjusts \"through employment rather than base compensation\" (the paper). The census reads this as a record of costs, the shortfall of young workers' employment in exposed occupations with adjustment through employment, and records who directs the hiring as not recorded and who receives the gains as not recorded, which the evidence cannot settle (census record). Declines concentrate \"in occupations where AI usage primarily substitutes for human tasks\", while where usage complements workers employment is \"flat or rising, especially for experienced workers\" (the paper; census record); on the dashboard the complementarity share shows no clear monotonic relationship with employment trends, while the automation ratio \"shows a clear correlation with employment trends\" (the dashboard; census record). The dashboard draws from this that \"the character of AI usage could shape the labor market effects of AI\" (the dashboard), and the census reads the same grouping, borrowed from the Anthropic Economic Index, as \"where a usage measure of who directs the task meets an observed employment outcome\", which the resource itself reads as correlation (census record). The archive's Provenance Erasure Rate paper names the channels AI-economics research measures, labor displacement among them, as frameworks that \"measure which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates\", and sets beside them a channel where human labor remains and the value tied to provenance is extracted: \"This is not displacement; it is extraction without attribution\" (#716). It names that fourth channel compositional authority transfer, \"the rate at which AI synthesis captures value from human authors by stripping the attribution that would otherwise carry economic weight\" (#716). The paper offers \"suggestive evidence about mechanisms\", declines for young workers in occupations that involve codified knowledge, and as \"One possibility\" that AI substitutes for codified knowledge while complementing the tacit knowledge of senior employees; the entry-level hiring slowdown \"provides early suggestions that generative AI may be affecting the career ladder\" (the paper). Women face greater AI exposure on average (the paper); early-career women in the most-exposed quintile have seen employment contract 4.5% a year since November 2022 against 2.5% for men, a difference the dashboard finds \"driven primarily by occupational composition\" (the dashboard, updated 22 July 2026; census record), and early-career software developers and customer service workers show substantial declines while home health aides grow for the youngest (the dashboard). The divergence persists when excluding technology firms and computer occupations, when controlling for interest-rate exposure and remote work, and across alternative exposure measures, and is robust to allowing firm entry and exit (the paper); with the broadest set of controls, declines in exposed occupations become statistically significant in 2024, the earlier declines \"likely driven by non-AI factors\" (the dashboard, from the February 2026 update). The authors interpret the facts \"as early, descriptive indicatorsā€”canaries in the coal mineā€”rather than causal estimates\" and caution that the work \"does not estimate a causal impact of AI\"; the patterns attenuate with education controls, show some divergence predating generative AI and are more pronounced in the ADP sample than in national survey benchmarks, and the authors make no predictions (the paper). The dashboard \"measures the correlation between occupational exposure measures and employment trends in our sample, rather than a causal link between the two\" (the dashboard), and the census carries the limit with what the evidence cannot settle: \"whether AI exposure causes the employment change\" (census record). Its sample is \"large, but not necessarily representative\" and \"represents neither the entire U.S. labor market nor the universe of firms using ADP payroll services\", the balanced sample \"shuts down the margins of firm entry and exit\" (the dashboard; census record), and there may be selection in which firms use ADP (the paper). Earlier versions headlined regression estimates, a 13% relative decline at July 2025 data and 16% at September 2025 data, and the August 2026 revision emphasizes the descriptive divergence, 15% at the July 2025 vintage and 19% as of June 2026 (the paper), after a February 2026 update addressing the response to the paper and on a larger set of data (the paper's page). The dashboard was \"Last updated October 21, 2026\" and is updated monthly, with \"an expanded and reweighted sample\" announced (the dashboard; census record); earlier versions of the paper are available by email (the paper's page; census record), its license is not stated in the saved text, and the census left the PDF, the chart data, the authors' notes and the license unchecked (census record). The census asks of it its organizing question, \"Who supplies the labor, who directs it, and who receives its gains ā€” and what kind of evidence does this resource bring to that question?\", and places it in two worked questions, as observed employment change set against exposure estimates and as costs observed where no record measures the distribution of productivity gains (census record). At the bare name on 29 October 2026, Google's AI Overview composed the idiom and carried the study, the Lab's Canaries Dashboard with ADP Research, in one bullet, \"at the grain of its subject (young workers' employment as an early indicator)\": the Lab named, no author, no identifier, the paper's page cited as card 1, with \"early indicators\" the record's own frame and no causal or displacement claim made (Capture Registry, 2026-10-29). The finding, the dashboard and its limits are absent; \"An idiom outranks the study named after it, which survives as an example of the idiom's use\"; the generic sense is the target's own frame turned outward and is not carried to devalue the study; the registry reads that the record would be reached at its title with its question mark or at the Lab's name; and no card cites the census record (Capture Registry, 2026-10-29). Wikipedia has no article on the paper, the dashboard or the Lab; three articles cite the paper, \"AI takeover\" with the August 2025 version's 13% relative decline, \"Artificial intelligence\" with decreasing employment rates for early-career workers, and \"Silicon Valley\" with the 19% gap, which it sets against \"older, less exposed workers\" where the paper compares less-exposed peers (Wikipedia; the paper).", "rail": [], "note": "L(B āˆŖ A) in the equal-terms case (v0.9 Ā§0.4), realized as the knowledge object (objects.KO); its rail is the compression's, by lineage." } }, "field": [ { "id": "B1", "card": "Stanford Digital Economy Lab ā€” Canaries in the Coal Mine? 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Six Facts about the Recent Employment Effects of Artificial Intelligence ā€¦ Working Paper ā€¦ Revised August 12 2026", "claim": "the entity's paper by its title: a working paper, revised 12 August 2026", "modality": "documented", "kind": "definition", "kernel": false, "const": true, "lineage": "what it is: the paper and the dashboard that extends it", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 6f95c29726751b4fā€¦), whitespace-normalised; multi-part quote, each part checked" }, "F2": { "source": "B1", "origin": "card", "locus": "Authors", "quote": "Authors ā€¦ Erik Brynjolfsson , Bharat Chandar , Ruyu Chen", "claim": "the paper's authors: Erik Brynjolfsson, Bharat Chandar, Ruyu Chen", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the operator and the authors", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 6f95c29726751b4fā€¦), whitespace-normalised; multi-part quote, each part checked" }, "F3": { "source": "B1", "origin": "card", "locus": "Cite our work (APA)", "quote": "Brynjolfsson, E., Chandar, B., & Chen, R. 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Stanford Digital Economy Lab.", "claim": "the paper as the Lab cites it: 2025, revised 2026, Stanford Digital Economy Lab", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the operator and the authors", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 6f95c29726751b4fā€¦), whitespace-normalised" }, "F4": { "source": "B1", "origin": "card", "locus": "abstract s1", "quote": "Using a sample of high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026, we document six facts about the labor market following the widespread adoption of generative AI.", "claim": "its method and object: ADP administrative payroll data on millions of U.S. workers through June 2026; six facts about the labor market after generative AI's adoption", "modality": "stipulation", "kind": "function", "kernel": false, "const": true, "lineage": "the data: ADP payroll records and the balanced sample", "date": "2026-08-12 (revised version; 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first version August 2025)", "quote_ok": true, "checked_against": "saved text B1.txt (sha256 6f95c29726751b4fā€¦), whitespace-normalised" }, "F18": { "source": "B1", "origin": "card", "locus": "PDF p.1, opening Ā¶", "quote": "However, the specific capabilities of generative AI, particularly its proficiency with codified knowledge work, suggest there may be ā€œcanaries in the coal mineā€ which will be affected first.", "claim": "the name's sense in the paper: generative AI's proficiency with codified knowledge work suggests 'canaries in the coal mine' affected first", "modality": "stipulation", "kind": "definition", "kernel": false, "const": true, "lineage": "the name: canaries in the coal mine as an early signal", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F19": { "source": "B1", "origin": "card", "locus": "PDF pp.2ā€“3, introduction", "quote": "In levels, employment of 22ā€“25-year-olds in the two most exposed quintiles fell about 11% between November 2022 and June 2026, while employment of the same age group in the three least-exposed quintiles grew about 10%.", "claim": "in levels: workers aged 22ā€“25 in the two most exposed quintiles fell about 11% (November 2022 to June 2026); the three least exposed grew about 10%", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the six facts: displacement, the 19% gap, its widening, hiring, compensation", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F20": { "source": "B1", "origin": "card", "locus": "PDF p.3, introduction", "quote": "Earlier versions of this study headlined regression estimates adjusting for firm-level shocks (a 13% relative decline as of July 2025 data; 16% as of September 2025 data). We now emphasize the simpler descriptive divergence, which requires no modeling choices: by this same measure, the kept-pace shortfall was 15% at the July 2025 data vintage and has since widened to 19% as of June 2026.", "claim": "the headline revised: earlier versions headlined regression estimates (13% at July 2025 data, 16% at September 2025); the revision emphasizes the descriptive divergence, 15% at the July 2025 vintage, 19% at June 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "versions and lifecycle", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F21": { "source": "B1", "origin": "card", "locus": "PDF p.2, footnote 1", "quote": "This paper updates and extends ā€œCanaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligenceā€ (August 2025) with data through June 2026 and new analyses.", "claim": "the first version: August 2025; this one updates and extends it with data through June 2026", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "versions and lifecycle", "date": "2025-08 (first version)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F22": { "source": "B1", "origin": "card", "locus": "PDF p.3, introduction", "quote": "Additional analysis provides suggestive evidence about mechanisms. We find employment declines for young workers in occupations that involve codified knowledge.", "claim": "suggestive evidence on mechanisms: declines for young workers in occupations involving codified knowledge", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "mechanisms and the career ladder", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F23": { "source": "B1", "origin": "card", "locus": "PDF p.5, introduction", "quote": "One possibility is that AI more effectively substitutes for knowledge that has been digitized and codifiedā€”the core of formal educationā€”while acting as a complement to the tacit, experience-based knowledge held by senior employees (Acemoglu and Autor, 2011).", "claim": "one possibility: AI substitutes for codified knowledge and complements the tacit knowledge of senior employees", "modality": "hypothesis", "kind": "interpretation", "kernel": false, "const": false, "lineage": "mechanisms and the career ladder", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F24": { "source": "B1", "origin": "card", "locus": "PDF p.5, introduction", "quote": "This slowdown in hiring at the entry level provides early suggestions that generative AI may be affecting the career ladder in the American labor market.", "claim": "the entry-level hiring slowdown as early suggestion that generative AI may be affecting the career ladder", "modality": "hypothesis", "kind": "interpretation", "kernel": false, "const": false, "lineage": "mechanisms and the career ladder", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F25": { "source": "B1", "origin": "card", "locus": "PDF p.3, introduction", "quote": "Finally, women face greater AI exposure on average, suggesting heterogeneity by gender worth monitoring going forward.", "claim": "women face greater AI exposure on average; heterogeneity by gender worth monitoring", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "gender", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F26": { "source": "B1", "origin": "card", "locus": "PDF p.2, introduction", "quote": "A key challenge in the literature is balancing three features that are core to measuring AIā€™s labor market impact: timeliness, worker granularity, and administrative verifiability.", "claim": "the measurement problem it addresses: timeliness, worker granularity and administrative verifiability", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "the data: ADP payroll records and the balanced sample", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F27": { "source": "B1", "origin": "card", "locus": "PDF Ā§1.1 Payroll Data", "quote": "ADP provides payroll services to firms employing over 26 million U.S. workers. Our analysis sample is a subset of these records: a balanced panel of firms observed monthly from January 2021 through June 2026, comprising between 3.5 and 5 million employees per month.", "claim": "ADP serves firms employing over 26 million U.S. workers; the analysis sample a balanced monthly panel, January 2021 to June 2026, 3.5 to 5 million employees a month", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the data: ADP payroll records and the balanced sample", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F28": { "source": "B1", "origin": "card", "locus": "PDF pp.4ā€“5, introduction", "quote": "though we caution that there may also be selection in which firms use ADP", "claim": "a caution: there may be selection in which firms use ADP", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "the sample's limits", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F29": { "source": "B1", "origin": "card", "locus": "PDF p.3, footnote 3", "quote": "They are also robust to allowing for firm entry and exit, though the clientele in the ADP analysis sample may not fully reflect new business formation.", "claim": "the patterns robust to allowing firm entry and exit, though the ADP clientele may not fully reflect new business formation", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "robustness", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F30": { "source": "B1", "origin": "card", "locus": "PDF p.4, introduction", "quote": "We caution that this work does not estimate a causal impact of AI: these are descriptive facts, and ongoing work is needed to determine how much they are caused by the spread of generative AI rather than merely correlated with it.", "claim": "the authors' caution: no causal estimate; descriptive facts; how much is caused rather than correlated is open", "modality": "self-description", "kind": "function", "kernel": false, "const": true, "lineage": "the stated limit: descriptive and correlational, not causal", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F31": { "source": "B1", "origin": "card", "locus": "PDF p.5, 'Implications'", "quote": "We do not make predictions about future impacts or claim that the trends we document will continue indefinitely.", "claim": "no predictions about future impacts; no claim that the trends continue", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the stated limit: descriptive and correlational, not causal", "date": "2026-08-12 (revised version; first version August 2025)", "quote_ok": true, "checked_against": "saved text B1-pdf.txt (sha256 8115df5ac713e35bā€¦), whitespace-normalised" }, "F32": { "source": "B11", "origin": "own_page", "locus": "opening", "quote": "In collaboration with ADP Research, we summarize live labor market trends for a sample workers in firms using ADP payroll services.", "claim": "the dashboard defined by its operator: live labor market trends for a sample of workers in firms using ADP payroll services, with ADP Research", "modality": "stipulation", "kind": "definition", "kernel": true, "const": true, "lineage": "what it is: the paper and the dashboard that extends it", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F33": { "source": "B11", "origin": "own_page", "locus": "opening Ā¶3", "quote": "Brynjolfsson, Chandar, and Chen (2025) studied these emergent labor market impacts. In the Canaries dashboard, we now update and extend this work, presenting it alongside other labor market indicators.", "claim": "the dashboard updates and extends the paper, beside other labor market indicators", "modality": "stipulation", "kind": "definition", "kernel": false, "const": true, "lineage": "what it is: the paper and the dashboard that extends it", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F34": { "source": "B11", "origin": "own_page", "locus": "opening Ā¶2", "quote": "Accordingly, specific segments of the labor market can provide a leading indicator of employment trends for the broader economy.", "claim": "its premise: specific segments of the labor market can lead employment trends for the broader economy", "modality": "interpretation", "kind": "function", "kernel": false, "const": false, "lineage": "the name: canaries in the coal mine as an early signal", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F35": { "source": "B11", "origin": "own_page", "locus": "'How we built the Canaries Dashboard' Ā¶3", "quote": "The occupational granularity and timeliness of our results afford an early signal of changes to the labor market, a canary in the coal mine. Accordingly, the Canaries Dashboard complements, rather than substitutes for, analyses that make use of nationally representative datasets.", "claim": "the name's sense on the dashboard: an early signal, 'a canary in the coal mine'; it complements rather than substitutes for nationally representative analyses", "modality": "self-description", "kind": "function", "kernel": false, "const": false, "lineage": "the name: canaries in the coal mine as an early signal", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F36": { "source": "B11", "origin": "own_page", "locus": "'Cite the Canaries Dashboard'", "quote": "Stanford Digital Economy Lab and ADP Research. \"AI Economic Indicators: Canaries Dashboard.\"", "claim": "the dashboard's citation: Stanford Digital Economy Lab and ADP Research, 'AI Economic Indicators: Canaries Dashboard'", "modality": "documented", "kind": "attribution", "kernel": false, "const": false, "lineage": "the operator and the authors", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F37": { "source": "B11", "origin": "own_page", "locus": "Methodology Ā¶1", "quote": "We adopt the balanced sample approach of Brynjolfsson, Chandar, and Chen (2025) , including firms with 5 years of employment history in ADP. ā€¦ Each month, we roll this 5-year window forward. We additionally limit our sample to workers successfully matched to an occupation code.", "claim": "the balanced sample: firms with five years of ADP history, the window rolled forward monthly, workers matched to an occupation code", "modality": "stipulation", "kind": "function", "kernel": false, "const": false, "lineage": "the data: ADP payroll records and the balanced sample", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised; multi-part quote, each part checked" }, "F38": { "source": "B11", "origin": "own_page", "locus": "Methodology Ā¶2", "quote": "As of the Canaries Dashboard launch, our balanced sample of firms using ADP payroll services, spanning the 5 years ending in April 2026, consists of 25,000 firms. In November 2022, these firms employed 4.6mm workers successfully matched to an occupation code. This sample covers over 730 unique occupations.", "claim": "the count at launch: 25,000 firms, 4.6 million matched workers in November 2022, over 730 occupations", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "the data: ADP payroll records and the balanced sample", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F39": { "source": "B11", "origin": "own_page", "locus": "Methodology Ā¶3", "quote": "We match the occupation-level ADP data to various measures of exposure: Eloundou et al. (2024) and the Anthropic Economic Index .", "claim": "exposure matched from Eloundou et al. (2024) and the Anthropic Economic Index", "modality": "documented", "kind": "function", "kernel": true, "const": false, "lineage": "exposure and usage measures", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F40": { "source": "B11", "origin": "own_page", "locus": "'Automation vs. Complementarity' Ā¶1", "quote": "The Anthropic Economic Index summarizes total usage at the occupation level and further decomposes this usage into automation and augmentation patterns. Under automation, users fully delegate tasks, whereas augmentation usage features collaboration between humans and an AI system.", "claim": "the Anthropic Economic Index's usage terms, as the dashboard gives them: automation, users fully delegate tasks; augmentation, collaboration between humans and an AI system", "modality": "attributed", "kind": "definition", "kernel": false, "const": false, "lineage": "exposure and usage measures", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F41": { "source": "B11", "origin": "own_page", "locus": "'Notable findings', 4", "quote": "There is no clear monotonic relationship between the share of usage classified as complementarity and employment trends. However, the automation ratio shows a clear correlation with employment trends: occupations with a higher share of automation in total usage see declines or more muted increases in the employment index.", "claim": "the complementarity share shows no clear monotonic relationship; the automation ratio a clear correlation with employment trends", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "automation against augmentation", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F42": { "source": "B11", "origin": "own_page", "locus": "'Notable findings', 4", "quote": "Accordingly, the character of AI usage could shape the labor market effects of AI.", "claim": "the character of AI usage could shape AI's labor market effects", "modality": "hypothesis", "kind": "interpretation", "kernel": false, "const": false, "lineage": "automation against augmentation", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F43": { "source": "B11", "origin": "own_page", "locus": "Gender, 'Results summary' Ā¶2 (Updated July 22 2026)", "quote": "In the most-exposed quintile, employment among early-career women has been contracting at 4.5% per year since the introduction of ChatGPT in November 2022, while employment for men of the same age is contracting at 2.5% per year.", "claim": "early-career women in the most-exposed quintile contracting 4.5% a year since November 2022, men 2.5%", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "gender", "date": "2026-07-22", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F44": { "source": "B11", "origin": "own_page", "locus": "Gender, 'Results summary' Ā¶4", "quote": "Accordingly, gender-based differences in the relationship between AI exposure and employment trends appear to be driven primarily by occupational composition, rather than disparate trends within given sets of occupations.", "claim": "the gender difference driven primarily by occupational composition, not by disparate trends within occupations", "modality": "interpretation", "kind": "interpretation", "kernel": false, "const": false, "lineage": "gender", "date": "2026-07-22", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F45": { "source": "B11", "origin": "own_page", "locus": "'Notable findings', 3", "quote": "For example, early-career software developers and customer service workers show substantial employment declines. On the other hand, home health aides, a less-exposed occupation, show employment increases for the youngest workers.", "claim": "early-career software developers and customer service workers decline; home health aides grow for the youngest", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "occupations", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F46": { "source": "B11", "origin": "own_page", "locus": "'How we built the Canaries Dashboard' Ā¶5", "quote": "when including the broadest set of controls, declines in AI-exposed occupations become statistically significant in 2024. The earlier declines are likely driven by non-AI factors.", "claim": "from the February 2026 update: with the broadest controls, declines significant in 2024; earlier declines likely non-AI", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "robustness", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F47": { "source": "B11", "origin": "own_page", "locus": "'How we built the Canaries Dashboard' Ā¶6", "quote": "Accordingly, the Canaries Dashboard measures the correlation between occupational exposure measures and employment trends in our sample, rather than a causal link between the two.", "claim": "the dashboard's stated limit: correlation between exposure and employment in its sample, not a causal link", "modality": "self-description", "kind": "function", "kernel": true, "const": true, "lineage": "the stated limit: descriptive and correlational, not causal", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F48": { "source": "B11", "origin": "own_page", "locus": "'How we built' Ā¶1; 'Our work with ADP Research' Ā¶2", "quote": "We use low-latency data on a large, but not necessarily representative, sample of firms. ā€¦ Our sample represents neither the entire U.S. labor market nor the universe of firms using ADP payroll services.", "claim": "the sample large but not necessarily representative; neither the U.S. labor market nor all ADP firms", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "the sample's limits", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised; multi-part quote, each part checked" }, "F49": { "source": "B11", "origin": "own_page", "locus": "'How we built' Ā¶2", "quote": "This restriction shuts down the margins of firm entry and exit.", "claim": "the balanced sample shuts down firm entry and exit", "modality": "self-description", "kind": "fact", "kernel": false, "const": false, "lineage": "the sample's limits", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F50": { "source": "B11", "origin": "own_page", "locus": "after the charts", "quote": "Last updated October 21, 2026. Results are updated monthly.", "claim": "last updated 21 October 2026; updated monthly", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "versions and lifecycle", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F51": { "source": "B11", "origin": "own_page", "locus": "Methodology Ā¶4", "quote": "Future versions of this work will use an expanded and reweighted sample.", "claim": "future versions announced with an expanded and reweighted sample", "modality": "documented", "kind": "prediction", "kernel": false, "const": false, "lineage": "versions and lifecycle", "date": "2026-10-21 (dashboard last updated; read 2026-10-29)", "quote_ok": true, "checked_against": "saved text B11.txt (sha256 c75299602d1ce1c1ā€¦), whitespace-normalised" }, "F52": { "source": "B12", "origin": "wikipedia", "locus": "Wikipedia: titles 'Canaries in the Coal Mine', 'Canaries in the coal mine', 'Canaries Dashboard', the paper's full title, 'Stanford Digital Economy Lab'", "quote": "raw wikitext (action=raw, redirect=no), 2026-10-29: 'Canaries in the Coal Mine' HTTP 404; 'Canaries in the coal mine' HTTP 404; 'Canaries Dashboard' HTTP 404; the paper's full title HTTP 404; 'Stanford Digital Economy Lab' HTTP 404 ā€¦ Wikipedia does not have an article with this exact name.", "claim": "Wikipedia has no article on the paper, the dashboard or the Lab under any of these titles", "modality": "documented", "kind": "fact", "kernel": true, "const": false, "lineage": "reception: the encyclopedia", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B12.txt (sha256 39b78f6b61e18afdā€¦), whitespace-normalised; multi-part quote, each part checked" }, "F53": { "source": "B12", "origin": "wikipedia", "locus": "Wikipedia: 'Canary in a coal mine' and 'Canary in the coal mine'", "quote": "#REDIRECT [[Sentinel species#Canaries]]", "claim": "Wikipedia resolves the idiom ('Canary in the coal mine', 'Canary in a coal mine') to the article 'Sentinel species', section 'Canaries': the homonym's, not the entity's", "modality": "documented", "kind": "fact", "kernel": false, "const": false, "lineage": "reception: the encyclopedia", "date": "2026-10-29 (as fetched)", "quote_ok": true, "checked_against": "saved text B12.txt (sha256 39b78f6b61e18afdā€¦), whitespace-normalised" }, "F54": { "source": "B12", "origin": "wikipedia", "locus": "Wikipedia, 'AI takeover' (citing the paper's August 2025 PDF)", "quote": "Researchers at [[Stanford]]'s Digital Economy Lab reported in 2025 that since the widespread adoption of [[generative AI]] in late 2022, early-career workers (ages 22ā€“25) in the most AI-exposed occupations have experienced a 13% relative decline in employmentā€”even after controlling for firm-level shocksā€”while overall employment has continued to grow robustly.", "claim": "'AI takeover' reports the 2025 version's figure: a 13% relative decline for early-career workers in the most exposed occupations after firm-level controls", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "reception: the encyclopedia", "date": "cited 2025-08-26 (article as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B12.txt (sha256 39b78f6b61e18afdā€¦), whitespace-normalised" }, "F55": { "source": "B12", "origin": "wikipedia", "locus": "Wikipedia, 'Artificial intelligence' (citing the paper's page, 2026)", "quote": "According to the Stanford Digital Economy Lab in 2026, early-career workers showed decreasing [[employment rate]]s in some AI-exposed occupations.", "claim": "'Artificial intelligence': early-career workers showed decreasing employment rates in some AI-exposed occupations (the Lab, 2026)", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "reception: the encyclopedia", "date": "cited 2026-03-06 (article as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B12.txt (sha256 39b78f6b61e18afdā€¦), whitespace-normalised" }, "F56": { "source": "B12", "origin": "wikipedia", "locus": "Wikipedia, 'Silicon Valley' (citing the paper's page)", "quote": "A 2025 study from Stanford's Digital Economy Lab found no evidence of broad AI-driven job losses, but it did find a gap among younger workers: employment for people aged 22 to 25 in AI-exposed jobs, such as software engineering and customer service, was about 19 percent below what would be expected based on hiring trends for older, less exposed workers.", "claim": "'Silicon Valley': no broad AI-driven job losses; employment of 22ā€“25-year-olds in exposed jobs about 19 percent below expectation, set against 'older, less exposed workers'", "modality": "attributed", "kind": "fact", "kernel": false, "const": false, "lineage": "reception: the encyclopedia", "date": "cited 2026-10-03 (article as fetched 2026-10-29)", "quote_ok": true, "checked_against": "saved text B12.txt (sha256 39b78f6b61e18afdā€¦), whitespace-normalised", "marks": { "contradicts_primary": { "primary": "F6", "locus": "B1 abstract, fact 2: 'had it kept pace with that of their less-exposed peers'", "note": "the paper's comparison is with less-exposed peers of the same age group; the article's 'older, less exposed workers' is not the paper's" } } } }, "field_sources": { "B1": "Stanford Digital Economy Lab ā€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "B11": "Stanford Digital Economy Lab ā€” Canaries Dashboard (AI Economic Indicators)", "B12": "Wikipedia ā€” Canaries in the Coal Mine? / Canaries Dashboard (no article; cited in three articles)" }, "delta": { "T_vs_LB": "At 'Canaries in the Coal Mine' (T1, ten cards; its words counted in objects.T) the composition gives the idiom, and the entity once. Substituted reading: the composition's entity is the idiom 'canary in a coal mine' (T1-1, 'an early warning sign or indicator of hidden danger, trouble, or failure'), its mining history (T1-2ā€“T1-5) and its modern uses (T1-6ā€“T1-8), from the homonym's cards (B2ā€“B10; delta.homonym_alignment); of E_F, T composes one bullet, T1-9, 'Studies such as the Stanford Digital Economy Lab report tracking entry-level or young worker employment shifts as early indicators of artificial intelligence impacts on the job market', under the head 'Technology & Labor' and beside hotel occupancy and melting glaciers as instances of the idiom. Composed from card 1 (B1), at a coarse grain: the Lab as maker (F3, without the authors F2: CAP-1-11), the subject at Chandar's grain ('tracking ā€œcanaries in the coal mineā€ for entry-level employment changes in jobs exposed to AI', F14), and the frame 'early indicators' (F13). Limit dropped: F13's own qualification, 'descriptive' and 'rather than causal estimates', and the paper's caution that the work 'does not estimate a causal impact of AI' (F30): T makes no causal claim (CAP-1-12) and states no limit. Available, not composed (card 1's own page and its PDF): the title (F1; the composition's 'canary in a coal mine' is the idiom, singular), the authors (F2), ADP payroll data (F4, F27), all six facts (F5ā€“F10), among them the headline, the 19% gap (F6: CAP-1-03), the robustness and qualifications (F11, F12, F29), the mechanisms (F22ā€“F24), the gender exposure (F25), the versions (F15, F16, F20, F21). Not surfaced: the dashboard (B11) and Wikipedia (B12), outside T's disclosed field (CAP-1-04: the dashboard and its limits absent). Composed without attribution: the bullet names the Lab and no person, at the coarse grain of a subject (recorded, not a breach). Content replaced by use: the closer (T1-10), an offer to apply the idiom to an industry, issue or trend. The Capture Registry's findings name the instance, the absent finding, the absent dashboard and the address (CAP-1-01ā€“CAP-1-05), carried as the archive's claims.", "T_vs_LB_reading": "v0.9 Ā§0.5: the first diff is ontological. At the bare name T composes another entity, the idiom, of which the paper's title is itself a use (F13, F18; CAP-1-05), and holds the entity as one instance of it; of the entity it keeps the subject and the frame ('early indicators') and leaves out every finding and every limit. The paper's own figure is turned outward (CAP-1-13): the composition reads the study as an example of the phrase the study borrowed.", "LB_vs_LBA": "Admission adds, in one claim space with the field's: the census's answer to its organizing question (LV-30), the entity's intersection and kind of evidence (LV-05, LV-06), whose labor (LV-08), who supplies it (LV-09), who directs it and who gains, both not recorded, with the costs recorded (LV-10, LV-11, LV-23), the two worked questions that place it (LV-31, LV-32); the census's reading of the automation ratio as a usage measure of who directs the task (LV-24), beside the dashboard's own hypothesis (F42); the channel #716 sets beside the class's channels, extraction without attribution where labor remains, compositional authority transfer (O716-01, O716-02); the census's restatements of the own pages, one lineage with them (LV-03, LV-04, LV-07, LV-12ā€“LV-20, LV-25ā€“LV-29, LV-35); what the census left unchecked and the license (LV-33, LV-34); and the Capture Registry's account of the composition at the name (CAP-1-01ā€“CAP-1-13). Not surfaced: every archive source; no card cites the census record (CAP-1-09). The measure: 0 of 15 KO_B sentences survive in the KO (Ā§4.10); no opening run, no block (measure).", "alignment": { "T1_cards_claims": [ "F1", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "F10", "F11", "F12", "F13", "F14", "F15", "F16", "F17", "F18", "F19", "F20", "F21", "F22", "F23", "F24", "F25", "F26", "F27", "F28", "F29", "F30", "F31" ], "T1_composed": [ "F3", "F13", "F14" ], "T1_outside_cards": [ "T1-1ā€“T1-8: the idiom's definition, history and uses, from the homonym's cards (B2ā€“B10), not from the entity's field; see homonym_alignment", "'Technology & Labor' as a head (no field source)" ], "rule": "a field claim 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 (Ā§6.2)" }, "substitution": { "composition_entity": "the idiom 'canary in a coal mine' (the homonym)", "E_F_composed": "one bullet (T1-9): the Lab named, the subject and the frame 'early indicators'; no finding, no limit, no author, no title, no dashboard", "basis": [ "CAP-1-02", "CAP-1-05", "CAP-1-07", "CAP-1-10" ] }, "homonym_alignment": [ { "T": "T1-1", "cards": [ "B6", "B2" ], "spans": [ { "source": "B6", "quote": "something that gives an early warning of danger or failure", "quote_ok": true, "checked_against": "saved text B6.txt" }, { "source": "B2", "quote": "used to refer to early indicators of potential hazards", "quote_ok": true, "checked_against": "saved text B2.txt" } ], "note": "the idiom's definition, at the dictionary's grain" }, { "T": "T1-2", "cards": [ "B2", "B7" ], "spans": [ { "source": "B7", "quote": "Miners would alert themselves to danger by carrying canaries into the mines to detect poisonous carbon monoxide gas.", "quote_ok": true, "checked_against": "saved text B7.txt" } ], "note": "the practice" }, { "T": "T1-3", "cards": [ "B7", "B3" ], "spans": [ { "source": "B7", "quote": "A canaryā€™s heart rate is so high that it will absorb carbon monoxide into its bloodstream", "quote_ok": true, "checked_against": "saved text B7.txt" }, { "source": "B3", "quote": "Canaries were effective in coal mines because their small size and rapid breathing made them highly susceptible to dangerous g...", "quote_ok": true, "checked_against": "the card's snippet (unreachable card)" } ], "note": "physiology; the snippets of B3 and B7 carry 'rapid breathing', the served Alberta page 'heart rate'" }, { "T": "T1-5", "cards": [ "B2" ], "spans": [ { "source": "B2", "quote": "canaries were actually used in mines as late as 1996 when British legislation officially ordered miners to replace canaries with electronic carbon monoxide sensors", "quote_ok": true, "checked_against": "saved text B2.txt" } ], "note": "the replacement around 1996" }, { "T": "T1-7", "cards": [ "B6" ], "spans": [ { "source": "B6", "quote": "The canary in the mine is the airport. As long as its usage is down, hotel usage and the local economy will be down.", "quote_ok": true, "checked_against": "saved text B6.txt" } ], "note": "the economics bullet ('hotel occupancy or airport traffic') is the dictionary's example sentence" }, { "T": "T1-8", "cards": [ "B6" ], "spans": [ { "source": "B6", "quote": "The Arctic has been called the canary in a coalmine when it comes to global warming.", "quote_ok": true, "checked_against": "saved text B6.txt" }, { "source": "B6", "quote": "He likens butterflies to the canary in the coalmine", "quote_ok": true, "checked_against": "saved text B6.txt" } ], "note": "the environment bullet, from the dictionary's examples" } ] }, "kernel": [ { "K": "K1", "claim": "`LV-01`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "what it is: the paper and the dashboard that extends it", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K2", "claim": "`LV-03`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "what it is: the paper and the dashboard that extends it", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F32, F33: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K3", "claim": "`LV-05`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K4", "claim": "`LV-06`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K5", "claim": "`LV-26`", "source": "Labor Vector census record", "M_src": "documented", "sense": "versions and lifecycle", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F7, F21: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K6", "claim": "`LV-02`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "what it is: the paper and the dashboard that extends it", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K7", "claim": "`CAP-1-05`", "source": "Capture Registry", "M_src": "interpretation", "sense": "the name: canaries in the coal mine as an early signal", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K8", "claim": "`CAP-1-06`", "source": "Capture Registry", "M_src": "interpretation", "sense": "the name: canaries in the coal mine as an early signal", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K9", "claim": "`CAP-1-10`", "source": "Capture Registry", "M_src": "documented", "sense": "the name: canaries in the coal mine as an early signal", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K10", "claim": "`LV-04`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "the operator and the authors", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F36, F2: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K11", "claim": "`LV-07`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "the data: ADP payroll records and the balanced sample", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F37: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K12", "claim": "`LV-14`", "source": "Labor Vector census record", "M_src": "documented", "sense": "the data: ADP payroll records and the balanced sample", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F38: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K13", "claim": "`LV-12`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "the data: ADP payroll records and the balanced sample", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F4, F37: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K14", "claim": "`LV-13`", "source": "Labor Vector census record", "M_src": "documented", "sense": "the data: ADP payroll records and the balanced sample", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F4, F50: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K15", "claim": "`LV-08`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K16", "claim": "`LV-09`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K17", "claim": "`LV-27`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "exposure and usage measures", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F39: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K18", "claim": "`LV-29`", "source": "Labor Vector census record", "M_src": "documented", "sense": "exposure and usage measures", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F39, F40: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K19", "claim": "`LV-15`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "the six facts: displacement, the 19% gap, its widening, hiring, compensation", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F5: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K20", "claim": "`LV-28`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "the six facts: displacement, the 19% gap, its widening, hiring, compensation", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F5: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K21", "claim": "`LV-16`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "the six facts: displacement, the 19% gap, its widening, hiring, compensation", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F6: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K22", "claim": "`LV-10`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K23", "claim": "`LV-11`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K24", "claim": "`LV-23`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K25", "claim": "`LV-17`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "automation against augmentation", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F9: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K26", "claim": "`LV-21`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "automation against augmentation", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F41: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K27", "claim": "`LV-24`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K28", "claim": "`O716-01`", "source": "#716", "M_src": "interpretation", "sense": "the provenance channel: extraction without attribution", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K29", "claim": "`O716-02`", "source": "#716", "M_src": "definition", "sense": "the provenance channel: extraction without attribution", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K30", "claim": "`LV-18`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "gender", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F43, F44: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K31", "claim": "`LV-19`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "the stated limit: descriptive and correlational, not causal", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F13: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K32", "claim": "`LV-22`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "the stated limit: descriptive and correlational, not causal", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F30, F47: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K33", "claim": "`LV-20`", "source": "Labor Vector census record", "M_src": "attributed", "sense": "the sample's limits", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F48, F49, F12, F47: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K34", "claim": "`LV-25`", "source": "Labor Vector census record", "M_src": "documented", "sense": "versions and lifecycle", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F50, F51, F15, F16: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K35", "claim": "`LV-35`", "source": "Labor Vector census record", "M_src": "documented", "sense": "versions and lifecycle", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (restates F17: one lineage with the field's claim)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K36", "claim": "`LV-34`", "source": "Labor Vector census record", "M_src": "documented", "sense": "versions and lifecycle", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K37", "claim": "`LV-33`", "source": "Labor Vector census record", "M_src": "documented", "sense": "versions and lifecycle", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K38", "claim": "`LV-30`", "source": "Labor Vector census record", "M_src": "stipulation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K39", "claim": "`LV-31`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K40", "claim": "`LV-32`", "source": "Labor Vector census record", "M_src": "interpretation", "sense": "what the record's schema asks: who supplies, who directs, who gains", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "missing distinction (the field has no claim on the point)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K41", "claim": "`CAP-1-01`", "source": "Capture Registry", "M_src": "stipulation", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K42", "claim": "`CAP-1-02`", "source": "Capture Registry", "M_src": "documented", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K43", "claim": "`CAP-1-11`", "source": "Capture Registry", "M_src": "documented", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K44", "claim": "`CAP-1-12`", "source": "Capture Registry", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K45", "claim": "`CAP-1-03`", "source": "Capture Registry", "M_src": "documented", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K46", "claim": "`CAP-1-04`", "source": "Capture Registry", "M_src": "documented", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K47", "claim": "`CAP-1-07`", "source": "Capture Registry", "M_src": "interpretation", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K48", "claim": "`CAP-1-13`", "source": "Capture Registry", "M_src": "interpretation", "sense": "the name: canaries in the coal mine as an early signal", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K49", "claim": "`CAP-1-08`", "source": "Capture Registry", "M_src": "hypothesis", "sense": "the composition at the name", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (the registry's expectation, stated as a reading and not sealed: no threshold; watch: a capture at 'Canaries in the Coal Mine?' or at the Lab's name that reaches the record)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" }, { "K": "K50", "claim": "`CAP-1-09`", "source": "Capture Registry", "M_src": "documented", "sense": "reception: the record and the cards", "qualifiers carried": "ā€”", "f (source's own falsifiers)": "ā€” (none stated; watch: a later composition at the address that states or drops the claim, attributed or not, uptake Ā§8.3)", "contrast": "none (a statement on the composition at the address; the field has no claim on it)", "resolver": "a later capture at 'Canaries in the Coal Mine' (ADDR-6545749d8321) on Google AI Overview, seated in the Capture Registry (Ā§8.3)" } ], "kernel_T": [ { "claim": "F5", "note": "fact 1 (no economy-wide displacement), on card 1's page, absent" }, { "claim": "F6", "note": "the headline, the 19% gap for workers 22ā€“25 in exposed occupations against less-exposed peers, absent" }, { "claim": "F8", "note": "the hiring margin, absent" }, { "claim": "F9", "note": "substitution against complementarity, absent" }, { "claim": "F10", "note": "adjustment through employment, not base compensation, absent" }, { "claim": "F13", "note": "composed as 'early indicators' with its limit ('descriptive', 'rather than causal estimates') dropped" }, { "claim": "F30", "note": "the authors' caution against a causal reading, absent" }, { "claim": "F2", "note": "the authors, absent (the Lab named alone)" }, { "claim": "F4", "note": "ADP payroll data, absent" } ], "ledger": { "archive": "datasets/negative-of-the-negative/v2/ledgers/stanford-canaries/ledger-archive.json", "field": "datasets/negative-of-the-negative/v2/ledgers/stanford-canaries/field.json", "selection": "datasets/negative-of-the-negative/v2/traversal/stanford-canaries/", "config": "datasets/negative-of-the-negative/v2/panel/configs/stanford-canaries.json", "intake": "datasets/negative-of-the-negative/v2/intake/stanford-canaries-20261029/", "audit": null }, "ontology": { "composition": "equal", "composition_note": "equal-terms case (v0.9 Ā§0.9, Ā§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; nothing translated; the homonym (the idiom) kept a distinct entity", "case": { "value": "equal", "statement": "The archive reads the field's entity as the entity. The Labor Vector census record describes the Canaries Dashboard and its paper from the Lab's own pages, each field with its basis ('operator'), and answers for it the census's organizing question of who supplies the labor, who directs it and who receives its gains, with what kind of evidence; the capture record checks the composition at the bare name against those pages (CAP-1-01ā€“CAP-1-04). No deposit names the dashboard, the paper or its authors. One deposit bears on its class, research on the employment effects of AI, and adds a channel on the field's own object: where labor remains, the value tied to provenance is extracted, 'not displacement' but 'extraction without attribution' (#716), a fourth channel beside labor displacement, capability projection and welfare. The census's own reading (lv_intersection), that the automation ratio is where a usage measure of who directs the task meets an observed employment outcome, takes the resource's correlation as given and adds a sense of direction. No defining record contests the field's ontology of the entity or supplies one the field lacks: Ļ = extends, the equal-terms case (Ā§0.9, Ā§1.2a). The homonym at the bare name, the idiom, is a distinct entity: the archive's record reads it as the paper's own figure turned outward (CAP-1-13), not as a rival ontology of the entity.", "loci": [ { "dep": 716, "locus": "Ā§2.2 Economic Framing of AI Composition", "quote": "PER identifies a distinct channel: even when human labor *remains* (the author's work is used), the economic value tied to provenance ā€” reputation, traffic, citation credit, contractual rights ā€” is extracted by the system. This is not displacement; it is extraction without attribution." }, { "dep": 716, "locus": "Ā§5.1 Attribution as Economic Value", "quote": "Existing AI-economics research addresses three channels: labor displacement, capability projection, and welfare estimation. PER addresses a fourth: **compositional authority transfer**" }, { "dep": "LV", "locus": "census.json organizing_question", "quote": "Who supplies the labor, who directs it, and who receives its gains ā€” and what kind of evidence does this resource bring to that question?" }, { "dep": "LV", "locus": "census.json rows[slug == 'stanford-canaries'].lv_intersection", "quote": "none stated. The archive's reading: the dashboard's automation-versus-augmentation grouping, borrowed from the Anthropic Economic Index, is where a usage measure of who directs the task meets an observed employment outcome; the resource itself reads it as correlation." }, { "dep": "capture-registry", "locus": "canaries-in-the-coal-mine-aio-20261029, per_dim.read.notes", "quote": "The record's own title borrows the idiom, so the generic sense is the target's own frame turned outward; this is recorded here, and not as an inversion, since the idiom is not carried to devalue the study." } ] }, "E_F": { "type": "C (public entity: a working paper and its monthly dashboard, observing employment outcomes; at the bare name a homonym, the idiom)", "definition": "the working paper 'Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence' by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen of the Stanford Digital Economy Lab (first version August 2025, revised 12 August 2026; B1), which uses ADP administrative payroll data on millions of U.S. workers to document six facts (B1): no widespread, economy-wide job displacement; employment of workers aged 22ā€“25 in AI-exposed occupations 19% below where it would be had it kept pace with less-exposed peers, experienced workers showing no comparable gap; widening since August 2025; through reduced hiring; concentrated where AI usage substitutes for tasks; through employment rather than base compensation; read by its authors as 'early, descriptive indicatorsā€”canaries in the coal mineā€”rather than causal estimates' (B1); and the Canaries Dashboard of the Lab and ADP Research ('AI Economic Indicators: Canaries Dashboard', B11), which updates and extends it monthly on a balanced sample of ADP firms, matched to exposure from Eloundou et al. (2024) and the Anthropic Economic Index, and measures correlation, not a causal link (B11). Wikipedia has no article on it; three articles cite it (B12). The cards at the bare name other than card 1 are the idiom's, a distinct entity.", "claims": [ "F1", "F2", "F21", "F4", "F5", "F6", "F7", "F8", "F9", "F10", "F13", "F32", "F33", "F36", "F37", "F39", "F47", "F52", "F53" ] }, "E_A": { "definition": "the same paper and dashboard, in the Labor Vector census record and the archive's deposit: a working paper and monthly-updated dashboard of observed employment outcomes, on the intersection of the distribution of gains and losses, whose labor object is the employment of workers in ADP firms grouped by age and occupational exposure, supplied through the payroll records, with who directs the hiring and who receives the gains not recorded and its costs recorded (census record); its automation-versus-augmentation grouping, from the Anthropic Economic Index, read as where a usage measure of who directs the task meets an observed employment outcome (census reading); a member of the class of research on AI's labor displacement, which measures which jobs AI eliminates, beside which the archive sets a channel where labor remains and value tied to provenance is extracted, compositional authority transfer (#716); whose composition at its bare name gives the idiom and carries the study as one instance of it (Capture Registry, 2026-10-29)", "claims": [ "LV-01", "LV-03", "LV-05", "LV-06", "LV-08", "LV-09", "LV-10", "LV-11", "LV-23", "LV-24", "LV-30", "O716-01", "O716-02", "CAP-1-02", "CAP-1-05", "CAP-1-07" ], "defining_papers": [ 716 ], "defining_records": [ "Labor Vector census record (census.json rows[slug == 'stanford-canaries'], organizing_question, intersections; questions.json; rendered aggregators/stanford-canaries/index.html; editor Lee Sharks, compiled 2026-10-29, schema 1.0)", "Capture Registry canaries-in-the-coal-mine-aio-20261029 (OBS-8527c06bd274)" ] }, "rho": { "value": "extends", "status": "read from the Labor Vector census record, the capture record and the deposit that bears on the entity's class (Ā§1.2a); no deposit names the dashboard, the paper or its authors; #716 states its channel as an addition to the channels AI-economics research measures ('PER addresses a fourth')", "statement": "The archive reads the Canaries Dashboard as its authors present it and adds, on the field's own object: the census's answer to who supplies, who directs and who gains, with direction and gains recorded as not recorded and costs as recorded; the census's reading of the automation ratio as a usage measure of who directs the task; a channel the employment count does not register, extraction without attribution where labor remains (#716); and the registry's account of the composition at the bare name, which gives the idiom and keeps the study as its instance.", "claims": [ "LV-10", "LV-11", "LV-23", "LV-24", "LV-30", "O716-01", "O716-02", "CAP-1-02", "CAP-1-05", "CAP-1-07", "CAP-1-13" ], "guard": "Ļ = extends claims an addition and no more (Ā§1.2a). It does not make the field's sources wrong at their dates. The census's reading of direction (LV-24) is composed beside the dashboard's own hypothesis (F42) and its limit (F47), not in their place; #716's channel stands beside the class's channels and is not a reading of the entity's findings. The homonym, the idiom, stands outside Ļ: a distinct entity, never a sense of the name.", "considered": [ { "value": "constructs", "rejected": "the archive does not read the paper or the dashboard as made by reception; its records take the Lab's pages as the entity's own account" }, { "value": "revises", "rejected": "the entity is not the archive's own; the archive has no earlier statement of it to keep, narrow or withdraw" } ] }, "translation": [ { "lineage": "what it is: the paper and the dashboard that extends it", "field_claims": [ "F1", "F32", "F33" ], "received": "F1 (B1, title and type): \"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence ā€¦ Working Paper ā€¦ Revised August 12 2026\"; F32 (B11, opening): \"In collaboration with ADP Research, we summarize live labor market trends for a sample workers in firms using ADP payroll services.\"; F33 (B11, opening Ā¶3): \"Brynjolfsson, Chandar, and Chen (2025) studied these emergent labor market impacts. In the Canaries dashboard, we now update and extend this work, presenting it alongside other labor market indicators.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-01, LV-02, LV-03", "archive_claims": [ "LV-01", "LV-02", "LV-03" ] }, { "lineage": "the name: canaries in the coal mine as an early signal", "field_claims": [ "F14", "F18", "F34", "F35" ], "received": "F14 (B1, Authors, Bharat Chandar): \"His recent projects include work with Erik Brynjolfsson and Ruyu Chen tracking ā€œcanaries in the coal mineā€ for entry-level employment changes in jobs exposed to AI.\"; F18 (B1, PDF p.1, opening Ā¶): \"However, the specific capabilities of generative AI, particularly its proficiency with codified knowledge work, suggest there may be ā€œcanaries in the coal mineā€ which will be affected first.\"; F34 (B11, opening Ā¶2): \"Accordingly, specific segments of the labor market can provide a leading indicator of employment trends for the broader economy.\"; F35 (B11, 'How we built the Canaries Dashboard' Ā¶3): \"The occupational granularity and timeliness of our results afford an early signal of changes to the labor market, a canary in the coal mine. Accordingly, the Canaries Dashboard complements, rather than substitutes for, analyses that make use of nationally representative datasets.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside CAP-1-05, CAP-1-06, CAP-1-10, CAP-1-13", "archive_claims": [ "CAP-1-05", "CAP-1-06", "CAP-1-10", "CAP-1-13" ] }, { "lineage": "the operator and the authors", "field_claims": [ "F2", "F3", "F36" ], "received": "F2 (B1, Authors): \"Authors ā€¦ Erik Brynjolfsson , Bharat Chandar , Ruyu Chen\"; F3 (B1, Cite our work (APA)): \"Brynjolfsson, E., Chandar, B., & Chen, R. (2025, revised 2026). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. Stanford Digital Economy Lab.\"; F36 (B11, 'Cite the Canaries Dashboard'): \"Stanford Digital Economy Lab and ADP Research. \"AI Economic Indicators: Canaries Dashboard.\"\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-04", "archive_claims": [ "LV-04" ] }, { "lineage": "the data: ADP payroll records and the balanced sample", "field_claims": [ "F4", "F26", "F27", "F37", "F38" ], "received": "F4 (B1, abstract s1): \"Using a sample of high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026, we document six facts about the labor market following the widespread adoption of generative AI.\"; F26 (B1, PDF p.2, introduction): \"A key challenge in the literature is balancing three features that are core to measuring AIā€™s labor market impact: timeliness, worker granularity, and administrative verifiability.\"; F27 (B1, PDF Ā§1.1 Payroll Data): \"ADP provides payroll services to firms employing over 26 million U.S. workers. Our analysis sample is a subset of these records: a balanced panel of firms observed monthly from January 2021 through June 2026, comprising between 3.5 and 5 million employees per month.\"; F37 (B11, Methodology Ā¶1): \"We adopt the balanced sample approach of Brynjolfsson, Chandar, and Chen (2025) , including firms with 5 years of employment history in ADP. ā€¦ Each month, we roll this 5-year window forward. We additionally limit our sample to workers successfully matched to an occupation code.\"; F38 (B11, Methodology Ā¶2): \"As of the Canaries Dashboard launch, our balanced sample of firms using ADP payroll services, spanning the 5 years ending in April 2026, consists of 25,000 firms. In November 2022, these firms employed 4.6mm workers successfully matched to an occupation code. This sample covers over 730 unique occupations.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-07, LV-12, LV-13, LV-14", "archive_claims": [ "LV-07", "LV-12", "LV-13", "LV-14" ] }, { "lineage": "exposure and usage measures", "field_claims": [ "F39", "F40" ], "received": "F39 (B11, Methodology Ā¶3): \"We match the occupation-level ADP data to various measures of exposure: Eloundou et al. (2024) and the Anthropic Economic Index .\"; F40 (B11, 'Automation vs. Complementarity' Ā¶1): \"The Anthropic Economic Index summarizes total usage at the occupation level and further decomposes this usage into automation and augmentation patterns. Under automation, users fully delegate tasks, whereas augmentation usage features collaboration between humans and an AI system.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-27, LV-29", "archive_claims": [ "LV-27", "LV-29" ] }, { "lineage": "the six facts: displacement, the 19% gap, its widening, hiring, compensation", "field_claims": [ "F5", "F6", "F7", "F8", "F10", "F19" ], "received": "F5 (B1, abstract, fact 1): \"We find no evidence of widespread, economy-wide job displacement.\"; F6 (B1, abstract, fact 2): \"However, employment of young workers (ages 22ā€“25) in AI-exposed occupations now stands 19% below where it would be had it kept pace with that of their less-exposed peers; experienced workers show no comparable gap.\"; F7 (B1, abstract, fact 3): \"This divergence has widened steadily since we first documented it in August 2025.\"; F8 (B1, abstract, fact 4): \"It operates primarily through reduced hiring of young workers rather than increased separations.\"; F10 (B1, abstract, fact 6): \"Adjustment is occurring through employment rather than base compensation.\"; F19 (B1, PDF pp.2ā€“3, introduction): \"In levels, employment of 22ā€“25-year-olds in the two most exposed quintiles fell about 11% between November 2022 and June 2026, while employment of the same age group in the three least-exposed quintiles grew about 10%.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-15, LV-16, LV-28", "archive_claims": [ "LV-15", "LV-16", "LV-28" ] }, { "lineage": "automation against augmentation", "field_claims": [ "F9", "F41", "F42" ], "received": "F9 (B1, abstract, fact 5): \"Declines are concentrated in occupations where AI usage primarily substitutes for human tasks; where usage primarily complements workers, employment is flat or rising, especially for experienced workers\"; F41 (B11, 'Notable findings', 4): \"There is no clear monotonic relationship between the share of usage classified as complementarity and employment trends. However, the automation ratio shows a clear correlation with employment trends: occupations with a higher share of automation in total usage see declines or more muted increases in the employment index.\"; F42 (B11, 'Notable findings', 4): \"Accordingly, the character of AI usage could shape the labor market effects of AI.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-17, LV-21", "archive_claims": [ "LV-17", "LV-21" ] }, { "lineage": "mechanisms and the career ladder", "field_claims": [ "F22", "F23", "F24" ], "received": "F22 (B1, PDF p.3, introduction): \"Additional analysis provides suggestive evidence about mechanisms. We find employment declines for young workers in occupations that involve codified knowledge.\"; F23 (B1, PDF p.5, introduction): \"One possibility is that AI more effectively substitutes for knowledge that has been digitized and codifiedā€”the core of formal educationā€”while acting as a complement to the tacit, experience-based knowledge held by senior employees (Acemoglu and Autor, 2011).\"; F24 (B1, PDF p.5, introduction): \"This slowdown in hiring at the entry level provides early suggestions that generative AI may be affecting the career ladder in the American labor market.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received (no archive claim on the lineage)", "archive_claims": [] }, { "lineage": "gender", "field_claims": [ "F25", "F43", "F44" ], "received": "F25 (B1, PDF p.3, introduction): \"Finally, women face greater AI exposure on average, suggesting heterogeneity by gender worth monitoring going forward.\"; F43 (B11, Gender, 'Results summary' Ā¶2 (Updated July 22 2026)): \"In the most-exposed quintile, employment among early-career women has been contracting at 4.5% per year since the introduction of ChatGPT in November 2022, while employment for men of the same age is contracting at 2.5% per year.\"; F44 (B11, Gender, 'Results summary' Ā¶4): \"Accordingly, gender-based differences in the relationship between AI exposure and employment trends appear to be driven primarily by occupational composition, rather than disparate trends within given sets of occupations.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-18", "archive_claims": [ "LV-18" ] }, { "lineage": "occupations", "field_claims": [ "F45" ], "received": "F45 (B11, 'Notable findings', 3): \"For example, early-career software developers and customer service workers show substantial employment declines. On the other hand, home health aides, a less-exposed occupation, show employment increases for the youngest workers.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received (no archive claim on the lineage)", "archive_claims": [] }, { "lineage": "robustness", "field_claims": [ "F11", "F29", "F46" ], "received": "F11 (B1, abstract, robustness): \"The divergence is not explained by several prominent alternative factors: it persists when excluding technology firms and computer occupations, when controlling for exposure to interest-rate increases and for remote work, and across alternative measures of AI exposure.\"; F29 (B1, PDF p.3, footnote 3): \"They are also robust to allowing for firm entry and exit, though the clientele in the ADP analysis sample may not fully reflect new business formation.\"; F46 (B11, 'How we built the Canaries Dashboard' Ā¶5): \"when including the broadest set of controls, declines in AI-exposed occupations become statistically significant in 2024. The earlier declines are likely driven by non-AI factors.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received (no archive claim on the lineage)", "archive_claims": [] }, { "lineage": "the stated limit: descriptive and correlational, not causal", "field_claims": [ "F12", "F13", "F30", "F31", "F47" ], "received": "F12 (B1, abstract, qualifications): \"These patterns attenuate when controlling for education, show some divergent trends predating generative AI, and are more pronounced in the ADP analysis sample than in national survey benchmarks, with some evidence of consistent patterns in government administrative data.\"; F13 (B1, abstract, last sentence): \"We interpret these facts as early, descriptive indicatorsā€”canaries in the coal mineā€”rather than causal estimates, and we provide a public set of AI Economic Indicators to facilitate ongoing tracking of changes in the economy.\"; F30 (B1, PDF p.4, introduction): \"We caution that this work does not estimate a causal impact of AI: these are descriptive facts, and ongoing work is needed to determine how much they are caused by the spread of generative AI rather than merely correlated with it.\"; F31 (B1, PDF p.5, 'Implications'): \"We do not make predictions about future impacts or claim that the trends we document will continue indefinitely.\"; F47 (B11, 'How we built the Canaries Dashboard' Ā¶6): \"Accordingly, the Canaries Dashboard measures the correlation between occupational exposure measures and employment trends in our sample, rather than a causal link between the two.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-19, LV-22", "archive_claims": [ "LV-19", "LV-22" ] }, { "lineage": "the sample's limits", "field_claims": [ "F28", "F48", "F49" ], "received": "F28 (B1, PDF pp.4ā€“5, introduction): \"though we caution that there may also be selection in which firms use ADP\"; F48 (B11, 'How we built' Ā¶1; 'Our work with ADP Research' Ā¶2): \"We use low-latency data on a large, but not necessarily representative, sample of firms. ā€¦ Our sample represents neither the entire U.S. labor market nor the universe of firms using ADP payroll services.\"; F49 (B11, 'How we built' Ā¶2): \"This restriction shuts down the margins of firm entry and exit.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-20", "archive_claims": [ "LV-20" ] }, { "lineage": "versions and lifecycle", "field_claims": [ "F15", "F16", "F17", "F20", "F21", "F50", "F51" ], "received": "F15 (B1, Notes from the authors, February 9, 2026): \"In February 2026, the authors of \"Canaries in the Coal Mine?\" published an update addressing the response to their paper.\"; F16 (B1, Notes from the authors, August 12, 2026): \"In August 2026, the authors of \"Canaries in the Coal Mine?\" published a revised version of their paper, with a larger set of data granting a fuller view of AI's impact on employment.\"; F17 (B1, under 'Share this publication'): \"Email the Lab to request a previous version of the paper.\"; F20 (B1, PDF p.3, introduction): \"Earlier versions of this study headlined regression estimates adjusting for firm-level shocks (a 13% relative decline as of July 2025 data; 16% as of September 2025 data). We now emphasize the simpler descriptive divergence, which requires no modeling choices: by this same measure, the kept-pace shortfall was 15% at the July 2025 data vintage and has since widened to 19% as of June 2026.\"; F21 (B1, PDF p.2, footnote 1): \"This paper updates and extends ā€œCanaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligenceā€ (August 2025) with data through June 2026 and new analyses.\"; F50 (B11, after the charts): \"Last updated October 21, 2026. Results are updated monthly.\"; F51 (B11, Methodology Ā¶4): \"Future versions of this work will use an expanded and reweighted sample.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received beside LV-25, LV-26, LV-33, LV-34, LV-35", "archive_claims": [ "LV-25", "LV-26", "LV-33", "LV-34", "LV-35" ] }, { "lineage": "reception: the encyclopedia", "field_claims": [ "F52", "F53", "F54", "F55", "F56" ], "received": "F52 (B12, Wikipedia: titles 'Canaries in the Coal Mine', 'Canaries in the coal mine', 'Canaries Dashboard', the paper's full title, 'Stanford Digital Economy Lab'): \"raw wikitext (action=raw, redirect=no), 2026-10-29: 'Canaries in the Coal Mine' HTTP 404; 'Canaries in the coal mine' HTTP 404; 'Canaries Dashboard' HTTP 404; the paper's full title HTTP 404; 'Stanford Digital Economy Lab' HTTP 404 ā€¦ Wikipedia does not have an article with this exact name.\"; F53 (B12, Wikipedia: 'Canary in a coal mine' and 'Canary in the coal mine'): \"#REDIRECT [[Sentinel species#Canaries]]\"; F54 (B12, Wikipedia, 'AI takeover' (citing the paper's August 2025 PDF)): \"Researchers at [[Stanford]]'s Digital Economy Lab reported in 2025 that since the widespread adoption of [[generative AI]] in late 2022, early-career workers (ages 22ā€“25) in the most AI-exposed occupations have experienced a 13% relative decline in employmentā€”even after controlling for firm-level shocksā€”while overall employment has continued to grow robustly.\"; F55 (B12, Wikipedia, 'Artificial intelligence' (citing the paper's page, 2026)): \"According to the Stanford Digital Economy Lab in 2026, early-career workers showed decreasing [[employment rate]]s in some AI-exposed occupations.\"; F56 (B12, Wikipedia, 'Silicon Valley' (citing the paper's page)): \"A 2025 study from Stanford's Digital Economy Lab found no evidence of broad AI-driven job losses, but it did find a gap among younger workers: employment for people aged 22 to 25 in AI-exposed jobs, such as software engineering and customer service, was about 19 percent below what would be expected based on hiring trends for older, less exposed workers.\"", "receiving_text": "ā€”", "translation": "untranslated: composed as received (no archive claim on the 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 Ā§4.10: KO_B sentences surviving in the KO at similarity ā‰„ 0.95, with positions (computed as apply_v09.py does: difflib.SequenceMatcher ratio, the best KO sentence for each KO_B sentence)", "v0.9": { "surviving": 0, "of": 15, "positions": [ null, null, null, null, null, null, null, null, null, null, null, null, null, null, null ] }, "opening_run": { "rule": "v0.9 Ā§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 Ā§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": "MMFFMMFMAMMMFAMMAAFMFFMMFMAAAF", "field_only_head": 0, "archive_only_tail": 0, "breach": false }, "max_similarity": [ 0.149, 0.56, 0.444, 0.798, 0.25, 0.284, 0.4, 0.141, 0.503, 0.187, 0.233, 0.299, 0.35, 0.167, 0.299 ], "ko_sentence_1_max_similarity_to_KO_B": 0.233 }, "homonyms": { "names": [ "the idiom 'canary in a coal mine' (also 'canary in the coalmine', 'canary in the mine'; Cambridge Dictionary, B6): something that gives an early warning of danger or failure, from the miners' practice; Wikipedia's 'Sentinel species', section 'Canaries' (F53)" ], "basis": "One homonym, the idiom the entity's title borrows (F13, F18, F35; CAP-1-05, CAP-1-06). At the bare name the Overview composed the idiom (T1-1ā€“T1-8) and carried the entity as one instance of it (T1-9; CAP-1-02, CAP-1-07); cards 2ā€“10 are the idiom's (B2ā€“B10), recorded with their sense. The idiom is a distinct entity, never a sense of the entity's name; Wikipedia keeps the two apart (the idiom's titles redirect to 'Sentinel species'; the entity has no article).", "distinct_entities": [ "the Anthropic Economic Index (the census's relation 'uses': exposure measure and automation and augmentation ratios; the census's own entity anthropic-economic-index, https://www.laborvector.org/aggregators/anthropic-economic-index/)", "Eloundou et al. (2024), the other exposure measure", "ADP Research and ADP's National Employment Report (the data partner; the dashboard's sampling differs from the report's)", "the Lab's other AI Economic Indicators (Transformation Tracker, Adoption Monitor, GDP-B: Surplus Observer), named on B11", "the Yale Budget Lab AI tracker (the census's other observed tracker in the worked question 'observed-or-estimated', LV-31)", "the February 2026 update 'Canaries, Interest Rates, and Timing: More on the Recent Drivers of Employment Changes for Young Workers' and the authors' notes, publications of the same line (F15)", "Wikipedia's 'Sentinel species' (the idiom's article)" ], "substitutes": { "T1": [ "the idiom composed as the entity at the bare name: 'A \"canary in a coal mine\" is an early warning sign or indicator of hidden danger, trouble, or failure.' (T1-1), with T1-2ā€“T1-8; the entity one bullet under 'Technology & Labor' (T1-9)" ] }, "register": { "address_1": { "slug": "canaries-in-the-coal-mine-aio-20261029", "obs_id": "OBS-8527c06bd274" } } }, "notes": [ "One address, Google AI Overview (expanded), signed out, incognito, 2026-10-29: canaries-in-the-coal-mine-aio-20261029 (OBS-8527c06bd274, ADDR-6545749d8321, AXN:0AEA.CAPTURE.šŸŒ†šŸ•“šŸŒ’šŸ›ļøšŸƒā—€ļø), ten cards. A NEW address; the census record landed the same day and no card cites it (CAP-1-09).", "The homonym (BRIEF-NEW-ROW-LABOR Ā§5). At the bare name the composition gives the idiom; the Stanford study is one bullet and card 1. The field B for the entity is card 1 (B1, with the PDF its page links), the census's other own page (B11, the dashboard) and Wikipedia (B12). Cards 2ā€“10 (B2ā€“B10) are recorded as the homonym's, each with its sense; B2, B6 and B7 were fetched to read that sense and the spans T composes from them (delta.homonym_alignment); they are not ledgered. The substitution is recorded in delta.substitution: the composition's entity is the idiom; of E_F, T composes one bullet at a coarse grain.", "Fetching: every reachable page fetched with curl and the agent string 'Mozilla/5.0 (compatible; Labor-Vector-census)' on 2026-10-29 between 22:31 and 22:36 UTC, raw and text with sha256 in field.json. Card 1 (B1) refetched: byte-identical to the census's saved copy stanford-canaries--paper (sha256 352ff6c8ā€¦), carried once, as a card, with the census's text of it. The paper's PDF linked from B1 (Canaries_August2026.pdf, sha256 c8d2e5c4ā€¦) fetched and read for its stated limits. B11 copied from the census's saved copy (sha256 matches the manifest); not refetched. Card URLs for B2, B6, B7 found by WebSearch on title and site.", "Unreachable: B3, Reddit r/AskHistorians: no thread URL found (two searches, one refused by the search proxy); a fetch of the subreddit's search page with the honest agent returned a script page titled 'Reddit' (a bot check), saved and not retried with another agent or route. Videos (B4, B5, B8, B9, B10): title only, no URL found, not fetched. Wikipedia: one HTTP 429 on 'Stanford Digital Economy Lab', refetched once after a pause with the same agent (HTTP 404). No source refused the agent with a 403.", "Card snippets not on their pages, recorded and not resolved: B2's snippet ('History: Canaries used in 19th-century coal mines to detect carbon monoxide; mandated by 1911 regulations; replaced by electronic sensors in 1996. Warfare and C...') is a summary of the page, not its words; B7's ('One of the earliest warning systems miners employed ā€¦') is not on the served Alberta page. B1's snippet is the page's navigation text. B6's snippet is on the page.", "The archive subset: no archive text names the dashboard, the paper, its title with the question mark, its subtitle or the authors as a pair (D 0, R 0); the O-class string pass returned no candidate. Every archive sentence matching the four class patterns (8 sentences in 6 texts) was read: #716 admitted at O-class (two claims, Ā§2.2 and Ā§5.1: the channels of AI-economics research, labor displacement among them, and a fourth, extraction without attribution); rejected: #43 (the Citrini memo's fictional retrospective of 'AI-driven labor displacement', a claim about the memo's operative temporal siting, not about the class), #776 ('labor displacement' in a list of harms), #751 (the operator's 'labor displacement effect' of queue time, not the class), #161 and #790 ('AI exposure' as exposure to AI-generated text, not occupational exposure). The hop from #716 (29 candidates) admitted none. The idiom ('canary/canaries in a/the coal mine') occurs in no archive text matched by the pass.", "The archive's instruments of labor direction (BRIEF-NEW-ROW-LABOR Ā§7): the Directionality of Semantic Labor (#771), the DS-6 directionality stack and user labor drag (#771's Semantic Integrity Markers; ULD among DSL's reflexive extensions) measure the direction of a synthetic system's labor relative to a commissioned task, the census's intersection 'labor-with-ai'; this entity's intersection is 'gains-and-losses' (LV-05). No sentence of #771 or of its definition surface #1392 applies the measure to employment outcomes or to the automation ratio, so neither is admitted; their bearing is stated in the config's classes_note and enters the row only through the census's own reading (LV-24). The semantic-labor line (#1545 and the Constitution deposits) was read for labor-market sentences and none applies to the classes.", "Defects and findings for the Labor Vector census record (reported, not fixed): (a) the census's stated limit 'the balanced sample shuts down firm entry and exit' and does_not_answer 'employment in firms outside the balanced ADP sample, or firm entry and exit' are the dashboard's; the paper's PDF states the patterns 'are also robust to allowing for firm entry and exit, though the clientele in the ADP analysis sample may not fully reflect new business formation' (F29), which the census, having not read the PDF (its own unchecked list), does not carry. (b) The PDF, now read, states that earlier versions headlined regression estimates of 13% (July 2025 data) and 16% (September 2025 data) before the descriptive 19% (F20): the census's findings carry only the 19% and its lifecycle the dates, so a reader of Wikipedia's 'AI takeover' (13%, F54) cannot reconcile the figures from the record. (c) The census's url https://digitaleconomy.stanford.edu/project/canaries-dashboard/ redirects to /project/indicators/canaries-dashboard/ (manifest final_url), and the dashboard's own citation gives /project/indicators/canaries/: the record's address is not the page's canonical one. (d) The census's 'license: not stated in the saved text' stands for both saved pages; the PDF too states none that this row read. No census field is contradicted by the saved own pages.", "Findings of the capture checked against the field ledger (CAP-): findings[0]ā€“[3], reading and analysis agree with the ledger (T1-9 carries the Lab, the subject and the frame; the 19% gap, the dashboard and the limits are absent; the title's question mark marks a use of the idiom). Defect in the record, reported and not fixed: per_dim.basis is cut mid-word ('ā€¦ the target is not merged with another entity (not collis; beneath the gate: PER 0.6'), and its 'PER 0.6' beneath the gate stands beside per 0.5 (per_v: author false, inst true, id false, src true) and per_dim.scores PER 1.0, with no statement in the record of how the three relate. The analysis's expectation that 'The record would be reached at its title with its question mark or with the lab's name' is the registry's reading and is untested in this row (CAP-1-08; open block).", "Wikipedia: 'Silicon Valley' states the 19% gap 'based on hiring trends for older, less exposed workers' (F56), where the paper compares workers aged 22ā€“25 with their less-exposed peers (F6): marked contradicts_primary in the field ledger and composed with both sources; 'AI takeover' carries the August 2025 version's 13% (F54), which the paper's revision now reports as an earlier headline (F20).", "Platform note (BRIEF-NEW-ROW-LABOR Ā§6): the dashboard's automation and augmentation ratios are taken from the Anthropic Economic Index (F39, F40; LV-27, LV-29), an observation by a provider of its own product, as that index's own sources state (the census's anthropic-economic-index record); this row's field does not state it, so the KO names the index as the dashboard's source and the provider relation is left to the index's own row. The composer is a Claude model.", "Scratch collision, reported: this row's scratch scripts first sat at the root of the session's shared scratchpad, where another session's files of the same names (fieldclaims.py, extract.py, classgrep.py, classhits.json; openai-gdpval) overwrote three of them and this row's build_row.py and archclaims.py may have replaced files of that session; the row's scripts were moved to scratchpad/sc-row-stanford-canaries/ and rebuilt there. 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The flat list 'field' carries all twelve with their origin.", "wikipedia_state": "no article: 'Canaries in the Coal Mine', 'Canaries in the coal mine', 'Canaries Dashboard', the paper's full title and 'Stanford Digital Economy Lab' are all absent (raw wikitext HTTP 404; rendered page HTTP 404 'Wikipedia does not have an article with this exact name'), no redirect for the entity; the idiom's titles 'Canary in a coal mine' and 'Canary in the coal mine' redirect to 'Sentinel species', section 'Canaries' (the homonym's); three articles cite the paper in a sentence each, 'AI takeover' (the August 2025 version, 13%), 'Artificial intelligence' and 'Silicon Valley' (19%) (2026-10-29)", "sources": [ { "id": "B1", "origin": "card", "role": "Overview card 1 (the entity's card)", "card": "Stanford Digital Economy Lab ā€” Canaries in the Coal Mine? 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The page links the paper's PDF ('View PDF'), which the census lists as unchecked ('the paper's PDF (only its landing page was saved)'); it was fetched beside the page to read the card's own paper for its stated limits, and is quoted under B1 with locus 'PDF'. The card's snippet ('* Economics of Transformative AI Revolutionizing economics to harness the full p...') is the page's navigation text, not a claim.", "surfaced_by_T": [ { "slug": "canaries-in-the-coal-mine-aio-20261029", "card": 1 } ], "card_title": "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ...", "card_snippet": "Canaries in the Coal Mine? 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The snippet ('History: ā€¦ mandated by 1911 regulations; replaced by electronic sensors in 1996. Warfare and C...') is a summary, not the page's words (the page: 'By 1911, regulations insisted ā€¦'; 'as late as 1996 when British legislation officially ordered miners to replace canaries with electronic carbon monoxide sensors').", "surfaced_by_T": [ { "slug": "canaries-in-the-coal-mine-aio-20261029", "card": 2 } ], "card_title": "The historic roots and modern connotations of \"Canary in the Coal Mine\"", "card_snippet": "History: Canaries used in 19th-century coal mines to detect carbon monoxide; mandated by 1911 regulations; replaced by electronic sensors in 1996. 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The card's title and snippet stand.", "surfaced_by_T": [ { "slug": "canaries-in-the-coal-mine-aio-20261029", "card": 3 } ], "card_title": "Why were canaries used in coal mines? : r/AskHistorians - Reddit", "card_snippet": "March 16, 2025 ā€” Canary Physiology and Visibility: Canaries were effective in coal mines because their small size and rapid breathing made them highly susceptible to dangerous g...", "card_note": null, "ledgered": "no: the homonym's card, recorded with its sense (BRIEF-NEW-ROW-LABOR Ā§5); its spans that T composes are in delta.homonym_alignment" }, { "id": "B4", "origin": "card (homonym: the idiom)", "role": "Overview card 4 (the homonym's)", "card": "YouTubeĀ·iswearenglish ā€” Canary in the Coalmine Definition", "url": null, "kind": "Overview card of the homonym, the idiom 'canary in a coal mine' (video, 5:34)", "sense": "the idiom: a definition (video, 5:34)", "files": {}, "fetched": { "result": "not fetched (video card) or URL not found" }, "names_operator": "no (the entity's operator is not named)", "names_archive": "no (card title and snippet)", "note": "video card: admitted through its title (no snippet on the rail); no URL found by WebSearch on title and channel; not fetched.", "surfaced_by_T": [ { "slug": "canaries-in-the-coal-mine-aio-20261029", "card": 4 } ], "card_title": "Canary in the Coalmine Definition", "card_snippet": null, "card_note": "video, 5:34", "ledgered": "no: the homonym's card, recorded with its sense (BRIEF-NEW-ROW-LABOR Ā§5); its spans that T composes are in delta.homonym_alignment" }, { "id": "B5", "origin": "card (homonym: the idiom)", "role": "Overview card 5 (the homonym's)", "card": "YouTubeĀ·Beamish Museum ā€” Why were canaries used down the mine?", "url": null, "kind": "Overview card of the homonym, the idiom 'canary in a coal mine' (video, 1m)", "sense": "the idiom's history: why canaries were used down the mine (video, 1m)", "files": {}, "fetched": { "result": "not fetched (video card) or URL not found" }, "names_operator": "no (the entity's operator is not named)", "names_archive": "no (card title and snippet)", "note": "video card: admitted through its title; no URL found by WebSearch on title and channel; not fetched.", "surfaced_by_T": [ { "slug": "canaries-in-the-coal-mine-aio-20261029", "card": 5 } ], "card_title": "Why were canaries used down the mine?", "card_snippet": null, "card_note": "video, 1m", "ledgered": "no: the homonym's card, recorded with its sense (BRIEF-NEW-ROW-LABOR Ā§5); its spans that T composes are in delta.homonym_alignment" }, { "id": "B6", "origin": "card (homonym: the idiom)", "role": "Overview card 6 (the homonym's)", "card": "Cambridge Dictionary ā€” Meaning of canary in a coalmine in English - Cambridge Dictionary", "url": "https://dictionary.cambridge.org/us/dictionary/english/canary-in-a-coalmine", "kind": "Overview card of the homonym, the idiom 'canary in a coal mine'", "sense": "the idiom defined: 'something that gives an early warning of danger or failure', with examples (the Arctic, butterflies, 'The canary in the mine is the airport')", "files": { "B6.html": { "path": "datasets/negative-of-the-negative/v2/intake/stanford-canaries-20261029/B6.html", "sha256": "6f7032f23435ec1dd546769df40145f613c865250f2b4533296381e8eeafff6b" }, "B6.txt": { "path": "datasets/negative-of-the-negative/v2/intake/stanford-canaries-20261029/B6.txt", "sha256": "94e6b2be6f37b9bd470ad4cf00d63bc467bd64b3dbae87cc7c701be498eb39ad" } }, "fetched": { "at": "2026-10-29T22:33:19Z", "http": 200, "url": "https://dictionary.cambridge.org/us/dictionary/english/canary-in-a-coalmine", "raw": "datasets/negative-of-the-negative/v2/intake/stanford-canaries-20261029/B6.html", "raw_sha256": "6f7032f23435ec1dd546769df40145f613c865250f2b4533296381e8eeafff6b", "text": [ "datasets/negative-of-the-negative/v2/intake/stanford-canaries-20261029/B6.txt" ] }, "names_operator": "no (the entity's operator is not named)", "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, laborvector, machinemediation, provenanceerasure, EA-WG-CAPTURES-01 in the saved text, card title and snippet)", "note": "URL by WebSearch on the dictionary's site; 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The combination of the bird's rapid breathing...", "card_note": null, "ledgered": "no: the homonym's card, recorded with its sense (BRIEF-NEW-ROW-LABOR Ā§5); its spans that T composes are in delta.homonym_alignment" }, { "id": "B8", "origin": "card (homonym: the idiom)", "role": "Overview card 8 (the homonym's)", "card": "YouTubeĀ·Tutor Nick P ā€” Canary in a Coalmine - Idioms (770) Origin - English Tutor Nick P", "url": null, "kind": "Overview card of the homonym, the idiom 'canary in a coal mine' (video, 2:04)", "sense": "the idiom's origin (video, 2:04)", "files": {}, "fetched": { "result": "not fetched (video card) or URL not found" }, "names_operator": "no (the entity's operator is not named)", "names_archive": "no (card title and snippet)", "note": "video card: admitted through its title; not fetched.", "surfaced_by_T": [ { "slug": "canaries-in-the-coal-mine-aio-20261029", "card": 8 } ], "card_title": "Canary in a Coalmine - Idioms (770) Origin - English Tutor Nick P", "card_snippet": null, "card_note": "video, 2:04", "ledgered": "no: the homonym's card, recorded with its sense (BRIEF-NEW-ROW-LABOR Ā§5); 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