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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.

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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 3 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.

venueno affunlabeled
Self- and Peer Assessment in Massive Open Online Courses
Wilfried Admiraal, Bart Huisman, Maarten van de Ven
2014· article· en· International Journal of Higher Education· Computer Science
machine prediction:candidate · metaresearchconsensus · none
79
citations
venueno affunlabeled
Social Presence in Massive Open Online Courses
Oleksandra Poquet, Vitomir Kovanović, P. de Vries, Thieme Hennis, Srécko Joksimovíc, Dragan Gašević +1 more
2018· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
76
citations
affunlabeled
Driving data storytelling from learning design
Vanessa Echeverría, Roberto Martínez‐Maldonado, Roger Granda, Katherine Chiluiza, Cristina Conati, Simon Buckingham Shum
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
76
citations
venueno affunlabeled
MOOCs and OER in the Global South: Problems and Potential
Monty King, Mark Pegrum, Martin Forsey
2018· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · open_scienceconsensus · none
74
citations
affunlabeled
StreamWiki
Zhicong Lu, Seongkook Heo, Daniel Wigdor
2018· article· en· Proceedings of the ACM on Human-Computer Interaction· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
venueno affunlabeled
Investigating MOOCs through blog mining
Yong Chen
2014· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
fundno affno abstractunlabeled
MOOC—making and open educational practices
Laura Czerniewicz, Andrew Deacon, Michael Glover, Sukaina Walji
2016· article· en· Journal of Computing in Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
venueno affunlabeled
Goal Setting and MOOC Completion
Erwin Handoko, Susie Gronseth, Sara McNeil, Curtis J. Bonk, Bernard Robin
2019· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
venueno affno abstractunlabeled
Physics Education Research
G. W. F. Drake
2005· article· en· Canadian Journal of Physics· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
65
citations
affno abstractunlabeled
Innovations in Smart Learning
Elvira Popescu, Kinshuk Kinshuk, Mohamed Koutheaïr Khribi, Ronghuai Huang, Mohamed Jemni, Nian‐Shing Chen +1 more
2016· book· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
62
citations
affunlabeled
Setting learning analytics in context
Rebecca Ferguson, Doug Clow, Leah P. Macfadyen, Alfred Essa, Shane Dawson, Shirley Alexander
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
62
citations

How this was built: Screen · Findings · About