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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
Evidence
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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 1 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
Developing a computer science-specific learning taxonomy
Ursula Fuller, Colin G. Johnson, Tuukka Ahoniemi, Diana Cukierman, Isidoro Hernán Losada, Jana Jacková +5 more
2007· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · noneconsensus · none
279
citations
affunlabeled
Compiler Error Messages Considered Unhelpful
Brett A. Becker, Paul Denny, Raymond Pettit, Durell Bouchard, Dennis Bouvier, Brian Harrington +6 more
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
193
citations
affaboutunlabeled
STEAM education: student learning and transferable skills
Marja Gabrielle Bertrand, Immaculate Kizito Namukasa
2020· article· en· Journal of Research in Innovative Teaching & Learning· Computer Science
machine prediction:candidate · noneconsensus · none
132
citations
afffundunlabeled
GeneyTM
Arman Danesh, Kori Inkpen, Felix Lau, Keith Shu, Kellogg S. Booth
2001· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
129
citations
affunlabeled
Naturally occurring data as research instrument
Raymond Lister, Tony Clear, Dennis Bouvier, Paul Carter, Anna Eckerdal, Jana Jacková +5 more
2010· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · noneconsensus · none
127
citations
afffundunlabeled
Reviewing CS1 exam question content
Andrew Petersen, Michelle Craig, Daniel Zingaro
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
120
citations
affunlabeled
Algorithmic Literacy and the Role for Libraries
Michael Ridley, Danica Pawlick-Potts
2021· article· en· Information Technology and Libraries· Computer Science
machine prediction:candidate · noneconsensus · none
93
citations
affunlabeled
Ten quick tips for teaching programming
Neil C. C. Brown, Greg Wilson
2018· article· en· PLoS Computational Biology· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
afffundno abstractunlabeled
A Review of Serious Games for Programming
Michael A. Miljanovic, Jeremy S. Bradbury
2018· review· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
83
citations
afffundunlabeled
Can You Teach Me To Machine Learn?
Elisabeth Sulmont, Elizabeth Patitsas, Jeremy R. Cooperstock
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
80
citations
afffundunlabeled
RoboBUG
Michael A. Miljanovic, Jeremy S. Bradbury
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
78
citations
affno abstractunlabeled
Developing a computer science-specific learning taxonomy
Ursula Fuller, Colin G. Johnson, Tuukka Ahoniemi, Diana Cukierman, Isidoro Hernán Losada, Jana Jacková +5 more
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
78
citations
affunlabeled
CS girls rock
Sandy Graham, Celine Latulipe
2003· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
67
citations
affaboutunlabeled
A pedagogical model for STEAM education
Marja Gabrielle Bertrand, Immaculate Kizito Namukasa
2022· article· en· Journal of Research in Innovative Teaching & Learning· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affvenueunlabeled
Maker pedagogy and science teacher education
Shawn Michael Bullock, Andrea J. Sator
2015· article· en· Journal of the Canadian Association for Curriculum Studies· Computer Science
machine prediction:candidate · noneconsensus · none
57
citations
affunlabeled
Drop, Fail, Pass, Continue
Diane Horton, Michelle Craig
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affunlabeled
Programming in K-12 science classrooms
Pratim Sengupta, Amanda Dickes, Amy Voss Farris, Ashlyn Karan, David Martin, Mason Wright
2015· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
aboutno affunlabeled
What We Say vs. What They Do
Anita DeWitt, Julia Fay, Madeleine Goldman, Eleanor Nicolson, Linda Oyolu, Lukas Resch +7 more
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
46
citations

How this was built: Screen · Findings · About