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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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E-Learning and Knowledge Management
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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.

345 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.
345 works in the cohort · of 4,299,418page 1 of 7

Labels cover 0 of 345 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 345 of 345 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
The Web 2.0 way of learning with technologies
Herwig Rollett, Mathias Lux, Markus Strohmaier, Gisela Dösinger, Klaus Tochtermann
2007· article· en· International Journal of Learning Technology· Computer Science
machine prediction:candidate · noneconsensus · none
202
citations
affvenueunlabeled
Systemic Changes in Higher Education
George Siemens, Kathleen Matheos
2012· article· en· in education· Computer Science
machine prediction:candidate · noneconsensus · none
78
citations
venueno affunlabeled
Preparing Educators to Teach in a Digital Age
Mohsen Keshavarz, Andrea Ghoneim
2021· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
E-Learning in Higher Education
Dirk Morrison
2007· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Aprender ciencias en y para la comunidad
Wolff‐Michael Roth
2002· article· es· Enseñanza de las Ciencias Revista de investigación y experiencias didácticas· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
ELEARNING CURRENT SITUATION AND EMERGING CHALLENGES
Moncef Bari, Rachida Djouab, Chi Phu Hoa
2018· article· en· PEOPLE International Journal of Social Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
venueno affunlabeled
Using MOOCs at Learning Centers in Northern Sweden
Anders Norberg, Åsa Händel, Per Ödling
2015· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
E-Learning in Higher Education
Dirk Morrison
2011· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
On line (de)formation: e-learning disadvantages
Laureano Ralón, Marcelo Vieta, María Lucía Vásquez-de-Prada
2004· article· es· Comunicar· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About