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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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Retraction
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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 7 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
The New Paideia
Raj Boora
2002· 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
affno abstractunlabeled
eInclusion by means of Digital Game-Based Learning
Maja Pivec, Olga Dziabenko, Paul Kearney
2005· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Assessment of Teachers' Electronic Portfolios
Teddy Parvanova
2004· 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
affno abstractunlabeled
Technology-mediated Professional Learning
Susan E. Gibson, Charmaine Brooks
2012· 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
affno abstractunlabeled
Issues in the development of online teacher education
George Gadanidis, Sharon Rich
2002· 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
A Moodle federated Digital Learning Environment at UQAM
Gilles Boulet, Marina Caplain, JEAN-FRANÇOIS TREMBLAY
2012· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Going Paperless: Using eBooks in Faculty Development Workshops
Rebecca J. Hogue, Madeleine Montpetit, Colla J. MacDonald
2013· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
The Effect of Animated Concept Maps on Knowledge Transfer
Olusola Adesope, John C. Nesbit, Susan Olubunmi, Tolulope Adesope
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Scaffolding for Experts: Trial not Error
Judi McCuaig, Alana Cordick
2007· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Health Professions
machine prediction:candidate · metaresearchconsensus · none
0
citations
affno abstractunlabeled
Student Perceptions of Online Practicum
Gabrielle Wilcox, Jennifer Lock
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
affno abstractunlabeled
Apply Formal Concept Analysis to Teaching Material Extraction
Shaochun Li, Ko-Kang Chu, Maiga Chang
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
How could Web 2.0 be shaping web-assisted learning?
Gregory Fleet, Peter Wallace
2007· 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

How this was built: Screen · Findings · About