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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.

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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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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 54 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.

affno abstractunlabeled
Commentary by Fredericks
Suzanne Fredericks
2010· article· en· Clinical Nursing Research· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Work Ethic and Stress
Andrew J. Cutler, Angela Antohi-Kominek
2023· book-chapter· en· Advances in human resources management and organizational development book series· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Well-Being Index for Canada
Filomena Maggino
2023· book-chapter· en· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Job Loss and the Couple Experience of Coping
Kyle J. Schalk
2016· article· en· Canadian Journal of Counselling and Psychotherapy· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
10.53762/14370s52
Salman Khan, Asif Naveed Ranjha, Muhammad Mohsin
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affno abstractunlabeled
Labor market inequality in Canada
W Lawrence
2009· article· en· Docs.school Publications· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
TARGETTING RURAL CARDIAC PATIENTS
A E. Salter, M. Sharratt, Koorus Bookan
2003· article· en· Medicine & Science in Sports & Exercise· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Promoting Well-Being in the Workforce
Claire Mallette
2017· article· en· Sigma Theta Tau International's 28th International Nursing Research Congress· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
In Case You Haven't Heard…
2023· article· en· Mental Health Weekly· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
L’analyse du chômage par les flux
Juan A. Fernandez, Alain Saucier
2008· article· fr· Cahiers québécois de démographie· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Gender, household labour, and psychological distress
Wenting Tao
2008· article· en· University Library - University of Saskatchewan (University of Saskatchewan)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
PROSPERED Data Manual: Child Labour
Arijit Nandi, Efe Atabay, Ilona Vincent
2019· dataset· en· Harvard Dataverse· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About