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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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Online and Blended Learning
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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.

3,545 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.
3,545 works in the cohort · of 4,299,418page 21 of 71

Labels cover 14 of 3,545 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 3,545 of 3,545 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
Group Exams as Learning Tools
Jalal Kawash, Tamer N. Jarada, Mohammad Moshirpour
2020· article· en· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
Empire State College: The Development of Online Learning
Patricia J. Lefor, Meg Benke, Evelyn Ting
2001· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
afffundunlabeled
When Do Learners Study?
Varshita Sher, Marek Hatala, Dragan Gašević
2022· article· en· Journal of Learning Analytics· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
The Reflection for Multimedia Teaching
Yan Dong, Rongchun Li
2011· article· en· Asian Social Science· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
A Neophyte About Online Teaching
Karen V. Lee
2008· article· en· Qualitative Inquiry· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Technology in Distance Education
M. Gordon Hunter, Peter J. Carr
2002· article· en· Journal of Global Information Management· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
The Luddite Revolt continues
Jon Baggaley
2010· article· en· Distance Education· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Initiating the Learning Process
Patrick Labelle
2007· article· en· Internet Reference Services Quarterly· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
The Need of the Virtual Principal Amid the Pandemic
Lee A. Westberry, Tara Hornor, Kent Murray
2021· article· en· International Journal of Education Policy and Leadership· Social Sciences
machine prediction:candidate · noneconsensus · none
11
citations

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