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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Employment and Welfare Studies
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,193 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,193 works in the cohort · of 4,299,418page 51 of 64

Labels cover 13 of 3,193 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 3,193 of 3,193 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

aboutno affunlabeled
The Employment Situation, March
Murat Tasci, Beth Mowry
2008· article· en· Economic Trends· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Regional Inequalities
Trevor Tombe
2025· book-chapter· en· Oxford University Press eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Permanently Temporary
Fariah Chowdhury
2016· book-chapter· en· Advances in linguistics and communication studies· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
Precarious Employment in Rural Ontario
Valencia Gaspard
2017· article· en· Rural Review Ontario Rural Planning Development and Policy· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
What to do if you get injured or ill from work
Stéphanie Premji, Momtaz Begum, Ellen MacEachen, Alex Medley
2020· article· en· MacSphere (McMaster University)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Labor Market not So Anomalous After All
Murat Tasci, Mary Zenker
2011· article· en· Economic Trends· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Ethnic Inequality in Canada: Economic and Health Dimensions
Ellen M. Gee, Karen Kobayashi, Steven G. Prus
2007· article· en· Social and Economic Dimensions of an Aging Population Research Papers· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Employment quality and mortality in Canada
Faraz Vahid Shahidi, Alessandra T. Andreacchi, Anne Fuller, Alexandra Blair, Nancy Carnide, Marianne Harris +4 more
2025· article· en· Journal of Epidemiology & Community Health· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About