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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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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 10 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.

afffundunlabeled
A Game Engine in Pure Python for CS1
John Aycock, Etienne Kyle Pitout, Sarah Storteboom
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Makerspaces
Marguerite Koole, Jean-François Dionne, Evan Todd McCoy, Jordan Epp
2016· book-chapter· en· Advances in educational technologies and instructional design book series· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Explaining Algorithms
Tomasz Müldner, Elhadi Shakshuki
2006· article· en· International Journal of Distance Education Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affaboutunlabeled
Organization Of The Robo Toy Contest
André Clavet, François Michaud
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Exploring Common Writing Issues in Upper-Year Computer Science
Rehmat Munir, Francesco Strafforello, Niveditha Kani, Michael Kaler, Bogdan Simion, Lisa Zhang
2022· article· en· Proceedings of the 53rd ACM Technical Symposium on Computer Science Education· Computer Science
machine prediction:candidate · metaresearchconsensus · none
3
citations
afffundunlabeled
The programming curriculum within ISIS
Marion Deslandes Martineau, Patrick Charland, Hugo G. Lapierre, Olivier Arvisais, Chirine Chamsine, Vivek Venkatesh +1 more
2022· article· en· PLoS ONE· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
The academic enhancement program
Diana Cukierman, D. Thompson
2009· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Dynamic Decision-Making Model
Marlo Steed
2018· book-chapter· en· Advances in educational technologies and instructional design book series· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affaboutunlabeled
Maker Education
Marja Gabrielle Bertrand, Immaculate Kizito Namukasa
2022· article· en· International Journal of Online Pedagogy and Course Design· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
3
citations
affunlabeled
The 2022 Computer Olympiad
Hiroyuki Iida, Jonathan Schaeffer, I‐Chen Wu
2023· article· en· ICGA Journal· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Role of the Teacher-Librarian
Janette Hughes, Laura Morrison
2022· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
PrairieLearn in CS1: An Experience Report
Kezia Devathasan, Jason Kepler, Johnathan Warawa, Amy Penney, Isabella Tsui, Celina Berg
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Technology in University Mathematics Education
Carl Winsløw, Marianna Bosch, Alejandro S. González-Martín, Rongrong Huo
2024· book-chapter· en· Springer international handbooks of education· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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