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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
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.

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 14 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
Experience Report
Lisa Zhang, Michelle Craig, Mark Kazakevich, Joseph Jay Williams
2019· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affno abstractunlabeled
Graphic Computational Thinking in Descriptive Geometry
Hang Yu, Shan Hongbo, Hongmin Cai, Yuanjun He, Wenjun Zhang
2022· book-chapter· en· Lecture notes on data engineering and communications technologies· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Programming in the model
Maryam Maleki, Robert Woodbury
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Knowledge Building in robotics for STEM education
Ahmad Khanlari
2018· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
International Programming Committee
Mario R. Eden, John D. Siirola, Gavin Towler, Luke E. K. Achenie, Thomas Adams, Claire S. Adjiman +47 more
2014· book-chapter· en· Computer-aided chemical engineering/Computer aided chemical engineering· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Discovering Algorithms with Matrix Code
M. H. van Emden
2012· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Graphics programming in elm develops math knowledge & social cohesion.
Yingsheng Zhang, Anirudh Verma, Chinmay Sheth, Christopher William Schankula, Stephanie Koehl, Andrew D. Kelly +2 more
2018· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The transition from STEM to STEAM
Jayanta Banerjee
2020· article· en· 2020 ASEE Virtual Annual Conference Content Access Proceedings· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Reithrodontomys fulvescens J. A. Allen 1894
2020· article· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About