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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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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.

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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.

1,694 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.
1,694 works in the cohort · of 4,299,418page 32 of 34

Labels cover 10 of 1,694 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 1,694 of 1,694 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.

affno abstractunlabeled
Educational Data Mining
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
AI hype in the classroom
Lex Konnelly, Nathan Sanders
2024· article· en· Proceedings of the Linguistic Society of America· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
SIGCSE conference report
Adrienne Decker, Kurt Eiselt
2015· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Prediction Model of School Readiness
Iyad Suleiman, Maha Arslan, Reda Alhajj, Mick Ridley
2017· article· en· Journal of Information & Knowledge Management· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
MOOCs : Facts and Figures
Thierry Karsenti
2015· article· fr· Revue internationale des technologies en pédagogie universitaire· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
ICER 2019 call for participation
Robert McCartney, Andrew Petersen
2019· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Approaches to Measuring User Engagement
Heather L. O'Brien
2025· book-chapter· en· Synthesis lectures on information concepts, retrieval, and services· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Optimization of Search Environments for Learning Contexts
Jaurès S. H. Kameni, Bernabé Batchakui, Roger Nkambou
2022· article· en· International Journal on Advanced Science Engineering and Information Technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Table of Contents
2014· article· en· Recruiting & Retaining Adult Learners· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Learning from Data, and Tools for the Task
John H. Maindonald, W. John Braun, Jeffrey L. Andrews
2024· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Promising Practices in Online Testing
Alyssa Dirocco, David Hinger
2009· article· en· EdMedia: World Conference on Educational Media and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Online Instructor Development: A COOL story
Nick Baker, Freer John, Nobuko Fujita, Higgison Alicia, Mark Lubrick, Brandon M. Sabourin +2 more
2020· article· en· Collected Essays on Learning and Teaching· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
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