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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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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
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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 11 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

fundno affunlabeled
Patterns of persistence
John Whitmer, Eva Schiorring, P. R. James
2014· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
12
citations
venueno affunlabeled
Development of Open Textbooks Learning Analytics System
Deepak Prasad, Rajneel Totaram, Tsuyoshi Usagawa
2016· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
Challenges of Offering a MOOC from an LMIC
Aamna Pasha, Syed Hani Abidi, Syed Ali
2016· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Uncovering what matters
Bodong Chen, Monica Resendes
2014· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
affunlabeled
The Student Advice Recommender Agent: SARA.
Jim Greer, Stephanie Frost, Ryan Banow, Craig Thompson, Sara Kuleza, Ken Wilson +1 more
2015· article· en· International Conference on User Modeling, Adaptation, and Personalization· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
A Tour of Open Universities Through Literature
Francisco Javier Hinojo Lucena, Inmaculada Aznar Díaz, María Pilar Cáceres Reche, José María Romero Rodríguez
2019· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
A Systematic Review of Questionnaire-Based Quantitative Research on MOOCs
Mingxiao Lu, Tianyi Cui, Zhenyu Huang, Hong Zhao, Tao Li, Kai Wang
2021· review· en· The International Review of Research in Open and Distributed Learning· Computer Science
distilled prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
10
citations
affno abstractunlabeled
Distance Education and Open Universities
A. S. Kanwar, John Daniel
2010· book-chapter· en· Elsevier eBooks· Computer Science
distilled prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Participating by activity or by week in MOOCs
Alok Baikadi, Carrie Demmans Epp, Christian D. Schunn
2018· article· en· Information and Learning Sciences· Computer Science
distilled prediction:candidate · noneconsensus · none
10
citations

How this was built: Screen · Findings · About