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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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Education and Critical Thinking Development
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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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venuejournal
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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.

2,368 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.
2,368 works in the cohort · of 4,299,418page 36 of 48

Labels cover 9 of 2,368 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 2,368 of 2,368 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.

affno abstractunlabeled
Book Review: SONIA NIETO (Ed.). Why We Teach Now.
Gabriella Lancia
2017· article· en· McGill Journal of Education / Revue des sciences de l'éducation de McGill· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Innovation in clinical learning: The AM/PM model
Alena Grewal, Angela Silvestri-Elmore
2022· article· en· Journal of Nursing Education and Practice· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Types of Interests and Children Learning
Qin Yuandong
2006· article· en· Early childhood education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Preface
Rolf Reber
2016· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
Editorial: Junctures in Teaching and Learning
Jannik Haruo Eikenaar
2018· editorial· en· Collected Essays on Learning and Teaching· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
From “How to Teach” to “How to Learn”
Ling Jiang, Sun Dao-jin
2014· article· en· Higher education of social science· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Critical Thinking & Social Change
Tina Arsenault
2012· article· en· Divergent/Convergent· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
The Brilliance of Children
Pat Cordeiro, Leslie Sevey
2013· article· en· LEARNing Landscapes· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Planning Frameworks and Their Effect on Student Teaching
Ottolene Ricord, Carolyn A. Bowles
2010· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Teaching as Research Teaching as Scholarship
Xiujun Ouyang, XU Xue-fu
2014· article· en· Higher education of social science· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Comprehension
Paige Wregget, Adam Card, Ryan S. McCann, Jayne Gazzola, Rachel Hacault
2019· article· en· Inquiry Queen s Undergraduate Research Conference Proceedings· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Peer Influence
Professor Reda Alhajj, Professor Jon Rokne
2014· book-chapter· en· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Using Imagery in Learning: The Constructive Gift
Kelly Edmonds
2005· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About