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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
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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 29 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.

affvenueno abstractunlabeled
Work and Pay in Japan.
2001· article· en· Pacific Affairs· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
The Health Wedge and Labor Market Inequality
Amy Finkelstein, Casey McQuillan, Owen Zidar, Eric Zwick
2023· article· en· Brookings Papers on Economic Activity· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
venueno affno abstractunlabeled
Special issue on labour standards in India
Jens Lerche, Isabelle Guérin, Rajendu Srivastava
2012· article· en· Global Labour Journal· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affvenueunlabeled
Those Who Serve
Steven Tufts
2007· article· it· Labour / Le Travail· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Populations at Special Health Risk: Workers
Carles Muntaner, Faraz Vahid Shahidi, Il‐Ho Kim, Haejoo Chung, Joan Benach
2016· book-chapter· en· Elsevier eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affno abstractunlabeled
Unemployment Insurance: Lessons from Canada
Morley Gunderson, W. Craig Riddell
2001· book-chapter· en· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Travail, chômage et stigmatisation
Ginette Herman, David Bourguignon, Florence Stinglhamber, Dany Jourdan
2007· book-chapter· fr· De Boeck Supérieur eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Who are the working women in Canada's top 1%?
Elizabeth A. Richards
2019· article· en· Analytical Studies Branch Research Paper Series· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affvenueaboutunlabeled
The Hidden Work of Challenging Precarity
Kiran Mirchandani, Mary Jean Hande
2020· article· en· The Canadian Journal of Sociology· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About