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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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Intelligent Tutoring Systems and Adaptive Learning
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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.

789 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.
789 works in the cohort · of 4,299,418page 2 of 16

Labels cover 1 of 789 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 789 of 789 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
Computational Thinking in Mathematics Teacher Education.
George Gadanidis, Rosa Cendros, Lisa Floyd, Immaculate Kizito Namukasa
2017· article· en· Contemporary issues in technology and teacher education· Computer Science
machine prediction:candidate · noneconsensus · none
48
citations
affunlabeled
Artificial intelligence to support human instruction
Michael C. Mozer, Melody Wiseheart, Timothy P. Novikoff
2019· letter· en· Proceedings of the National Academy of Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affno abstractunlabeled
Modeling Tutoring Knowledge
Jacqueline Bourdeau, Monique Grandbastien
2010· book-chapter· en· Studies in computational intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
aboutno affunlabeled
Collaboration in cognitive tutor use in latin America
Amy Ogan, Erin Walker, Ryan S. Baker, Genaro Rebolledo‐Mendez, Maynor Jiménez-Castro, Tania Laurentino +1 more
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affno abstractunlabeled
Designing Virtual Learning Centers
Gilbert Paquette
2002· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
venueno affunlabeled
Using Topic Maps for Web-based Education
Christo Dichev, Darina Dicheva, Lora Aroyo
2004· article· en· Advanced Technology for Learning· Computer Science
machine prediction:candidate · noneconsensus · none
32
citations

How this was built: Screen · Findings · About