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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-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education
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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.

491 results · 1 filter active ·
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20022019
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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.
491 works in the cohort · of 4,299,418page 2 of 10

Labels cover 1 of 491 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 491 of 491 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.

affno abstractunlabeled
Cost Modeling for eLearning
Andrew Choi, Jon Hatol, Vive Kumar
2003· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Leadership in Distance Education: Do we need a Leadershift?
Heather Scarlett-Ferguson
2011· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Diagnostic Cognitive Assessment and E-learning
Kit Hang Leung
2002· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Rating Learning Object Quality with Bayesian Belief Networks
Kate Han, Vive Kumar, John C. Nesbit
2003· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Process-Oriented Assessment in Mathematics Education
Zekeriya Karadağ, Douglas McDougall
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Ten Principles for Effective Tinkering
Jon Dron
2014· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Modalities of Using Learning Objects for Intelligent Agents
Dorian Stoilescu
2007· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Online learning: Myths and realities
Qing Li
2004· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Using Laptops Effectively in Higher Education
Robin Kay
2004· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Designing a Mathematics-for-All MOOC
George Gadanidis
2013· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Toward Accessible Learning Resources
Olusola Adesope, John C. Nesbit
2005· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Assessing the Impact of a Virtual Lab In Health Care Education
Helene-Marie Goulding, Robin Kay, Jia Li
2016· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Connecting E-portfolios and Learner Models
Zinan Guo, Jim Greer
2005· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Online Feedback Strategies for Learning and Teaching
Salter Diane, Leslie Richards
2004· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About