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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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Social Media in Health Education
Retraction
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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
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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.

2,014 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.
2,014 works in the cohort · of 4,299,418page 29 of 41

Labels cover 32 of 2,014 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 2,014 of 2,014 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
Health Data and Privacy in the Digital Era
Lawrence O. Gostin, Sam Halabi, Kumanan Wilson
2018· article· en· eYLS (Yale Law School)· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
CCAHTE Journal converting to OA
Peter Suber
2007· preprint· en· Social Sciences
machine prediction:candidate · scholarly_communication+open_science+insufficient_payloadconsensus · none
0
citations
affunlabeled
Mining Twitter data to #educate the public about #sepsis
Simon Guienguere, Kirsten M. Fiest, Tyler Williamson, Christopher J. Doig
2018· article· en· International Journal for Population Data Science· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Healthcare in the Age of Google
Arthi Erika Jeyamohan, Sarah M. Durant, Victoria Settimi, Erika Campbell, Karen Lawford
2024· article· en· Turtle Island Journal of Indigenous Health· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
manifest.xml
Michelle Cullen, David Topps
2019· dataset· en· Harvard Dataverse· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About