MétaCan
ENFR
Menu
Cohort builder

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.

Export CSVExport JSONAPI query

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.

Search term
Author
Year range
→
Sort
Language
Type
Field
Venue
Topic
Health disparities and outcomes
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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
aboutaboutness

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

4,167 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
4,167 works in the cohort · of 4,299,418page 83 of 84

Labels cover 28 of 4,167 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 4,167 of 4,167 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.

affunlabeled
Social Epidemiology
Jay S. Kaufman
2022· other· en· The Blackwell Encyclopedia of Sociology· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
What makes great data documentation?
Mark Smith, Mahmoud Azimaee
2018· article· en· International Journal for Population Data Science· Social Sciences
machine prediction:candidate · metaresearch+open_scienceconsensus · none
0
citations
affunlabeled
Health Geography
Damian Collins, Joshua Evans
2017· other· en· International Encyclopedia of Geography· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Editorial comment
Catherine Dennison, Geraldine McCormick
2006· editorial· en· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About