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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 9 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
BOF: Grading for Equity in Computer Science Courses
Manuel A. Pérez-Quiñones, David L. Largent, Firas Moosvi, Christian Roberson, Carlo Sgro, Giulia Toti +1 more
2022· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Story-Based Virtual Experiment Environment
Ming-Xiang Fan, Rita Kuo, Maiga Chang, Jia-Sheng Heh
2014· book-chapter· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Student Perspectives on Optional Groups
Jonathan Calver, Jennifer Campbell, Michelle Craig
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Achievement Goals in CS1-LLM
Annapurna Vadaparty, Francis Geng, David H. Smith, Jamie Gorson Benario, Daniel Zingaro, Leo Porter
2025· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
"Mailing it in"
Joseph Sant
2009· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
4
citations
affunlabeled
The academic enhancement program
Diana Cukierman, D. Thompson
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Adding Test Generation to the Teaching Machine
Michael Bruce-Lockhart, Theodore S. Norvell, Pierluigi Crescenzi
2009· article· en· ACM Transactions on Computing Education· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
Searching for Tomorrow’s Programmers
Michael de Raadt
2004· article· en· Informing Science and IT Education Conference· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Computational Thinking and Mathematics
Laura Broley, Chantal Buteau, Simon Modeste, Maryna Rafalska, Max Stephens
2024· book-chapter· en· Springer international handbooks of education· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
How much choice is too much?
K. Becker
2006· article· en· 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
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
afffundno abstractunlabeled
Decomposed Prompting to Answer Questions on a Course Discussion Board
Brandon Jaipersaud, Jimmy Ba, Andrew Petersen, Lisa Zhang, Michael R. Zhang
2023· book-chapter· en· Communications in computer and information science· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
3
citations
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
Introducing Code Quality in the CS1 Classroom
Cruz Izu, Claudio Mirolo, Jürgen Börstler, Harold Connamacher, Ryan Crosby, Richard Glassey +9 more
2024· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About