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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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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.

1,694 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.
1,694 works in the cohort · of 4,299,418page 4 of 34

Labels cover 10 of 1,694 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 1,694 of 1,694 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
AAT
Sabine Graf, Cindy Ives, Nazim Rahman, Arnold Ferri
2011· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
57
citations
venueno affunlabeled
Massive open online courses for Africa by Africa
Benedict Oyo, Billy Mathias Kalema
2014· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
fundno affunlabeled
Video-Based Learning and Open Online Courses
Michail N. Giannakos, Konstantinos Chorianopoulos, Marco Ronchetti, Péter Szegedi, Stephanie D. Teasley
2014· article· en· International Journal of Emerging Technologies in Learning (iJET)· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
venueno affunlabeled
The Ethics of Big Data in Higher Education
Jeffrey Alan Johnson
2014· article· en· The International Review of Information Ethics· Computer Science
machine prediction:candidate · noneconsensus · none
52
citations
affno abstractunlabeled
Learning at Scale
Ido Roll, Daniel M. Russell, Dragan Gašević
2018· article· en· International Journal of Artificial Intelligence in Education· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affunlabeled
The Life Between Big Data Log Events
George Veletsianos, Justin Reich, Laura A. Pasquini
2016· article· en· AERA Open· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Teaching through a Global Pandemic
Angela A. Siegel, Mark Zarb, Bedour Alshaigy, Jeremiah Blanchard, Tom Crick, Richard Glassey +5 more
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
47
citations
affaboutunlabeled
Discussion Forums in MOOCs
Afsaneh Sharif, Barry Magrill
2015· article· en· International Journal of Learning Teaching and Educational Research· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations

How this was built: Screen · Findings · About