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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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Industry and Higher Education
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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
aboutaboutness

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

26 results · 1 filter active ·
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20022025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
26 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 26 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 26 of 26 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
The rigour–relevance gap in professional programmes
David Finch, Loren Falkenberg, Patricia Genoe McLaren, Kent V. Rondeau, Norm O’Reilly
2018· article· en· Industry and Higher Education· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
19
citations
affunlabeled
Theory, practice and policy
Qiantao Zhang
2018· article· en· Industry and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
When Did You Last Predict a Good Idea?
Kathryn Penaluna, Andy Penaluna, Colin Jones, Harry Matlay
2014· article· en· Industry and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
7
citations
fundno affno abstractunlabeled
<i>Special Issue</i> : Innovative Pedagogy in Entrepreneurship
Rita Klapper, Denis Feather, Deema Refai, John F. Thompson, Alain Fayolle
2015· article· en· Industry and Higher Education· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Academic and practitioner antecedents of scholarly outcomes
David Finch, Norm O’Reilly, David L. Deephouse, William Foster, Andrea Dubak, Jenna Shaw
2016· article· en· Industry and Higher Education· Business, Management and Accounting
machine prediction:candidate · metaresearchconsensus · none
5
citations
aboutno affunlabeled
When Do Start-ups Make Sense?
Clement J. Langemeyer
2005· article· en· Industry and Higher Education· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Gap Funding in the USA and Canada
Steven Price, P. Z. Sobocinski
2002· article· en· Industry and Higher Education· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
CEO and academic mismatch
Jason A. Aimone, Stanton Hudja, Blaine McCormick
2025· article· en· Industry and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About