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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.

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.

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 52 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.

venueaboutno affunlabeled
Message from the Editor-in-Chief
Ingrid Harrington
2024· article· en· International Journal of Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Technology Based Learning: Myth or Reality?
Michael Young, Michel Plaisent, Prosper Bernard
2002· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Blended Learning: Students' Perspectives
Cheryl Jeffs
2011· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Inquisitivism
Dwayne Harapnuik
2011· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
DOCUMENT RESUME
2016· article· en· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Online Learning: What does good teaching and learning look like?
Tess Miller, Charity Becker, Kendra MacLaren, Shari MacKenzie, Beth Robichaud, Barbara Brewster +1 more
2019· article· en· 2019 Conference of the Canadian Society for the Study of Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Enabling Technologies for Incidental Learning on the Web
Harris Wang
2014· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Web 2.0 Enabled Blended Learning
Michael Zeiller
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Editorial: eLearning Technologies and Resources in the Classroom
Stewart Marshall, Wal Taylor
2012· editorial· en· The International Journal of Education and Development using Information and Communication Technology (The University of the West Indies)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Managing Virtual Schools
Margaret Haughey, William Muirhead
2011· book-chapter· en· IGI Global eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Book Reviews [Book Review]
Kerry C Pratt, Mark E. Nichols, Andrew Higgins
2010· article· en· Journal of Open Flexible and Distance Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About