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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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Education Systems and Policy
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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.

2,366 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.
2,366 works in the cohort · of 4,299,418page 46 of 48

Labels cover 4 of 2,366 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 2,366 of 2,366 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.

affaboutunlabeled
Memorial University's Learners and Locations project: A community update
James Rourke, Kristen Harris Walsh, Danielle O’Keefe, Mohamed Ravaila, Scott Moffatt, Wanda Parsons +3 more
2017· article· en· The Journal of Macrodynamic Analysis (Memorial University of Newfoundland)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
CE a major feature of SEG/Calgary 2000
Jill Thompson
2000· article· en· The Leading Edge· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Pre-Service Teacher Perspectives on Course Delivery Formats
Denyse V. Hayward, Ewa Wasniewski
2015· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Indigenous land-based skills through the lens of augmented reality
Kirsten Pruzan Mikkelsen, Ali M AL-Asadi
2017· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
A Learning Model for Rural Yukon Educators
Alexandrea Postoloski
2019· article· en· Scholarship@Western (Western University)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
2005 CAG Educational Need Assessment Report
Ronald Bridges, Sandra Daniels
2005· article· en· Canadian Journal of Gastroenterology· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About