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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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Teaching and Learning Programming
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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
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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.

980 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.
980 works in the cohort · of 4,299,418page 4 of 20

Labels cover 2 of 980 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 980 of 980 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
Toward a K-12 computer science curriculum
Allen B. Tucker, Fadi P. Deek, Jill S. Jones, Dennis McCowan, Chris Stephenson, Anita Verno
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
How to Think About Algorithms
Jeff Edmonds
2008· book· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
afffundunlabeled
Crazy Like Us
Alissa N. Antle
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
afffundunlabeled
Addressing bullying through critical making
Janette Hughes, Laura Morrison, Ami Mamolo, Jennifer Laffier, Suzanne de Castell
2018· article· en· British Journal of Educational Technology· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
BitFit
Anthony Estey, Anna Russo Kennedy, Yvonne Coady
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
An Experience Report
Jennifer Campbell, Anya Tafliovich
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affno abstractunlabeled
Re-situating Constructionism
John W. Maxwell
2007· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Program Wars
John Anvik, Vincent Côté, Jace Riehl
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
EduCHI 2020: 2nd Annual Symposium on HCI Education
Olivier St-Cyr, Craig M. MacDonald, Colin M. Gray, Leigh Ellen Potter, Anna Vasilchenko, Jaisie Sin +1 more
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Answering the Correct Question
Michelle Craig, Andrew Petersen, Jennifer Campbell
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
aboutno affunlabeled
Status update
Lawrence Snyder
2012· article· en· ACM Inroads· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
12
citations
affunlabeled
Mobile Robots Engaging Children in Learning
François Michaud, Tamie Salter, Audrey Duquette, Henri Mercier, Michel Lauria, Hélène Larouche +1 more
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations

How this was built: Screen · Findings · About