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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 10 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
iClasse®: A classroom that engage students and teachers
Pierre Poulin
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
affaboutno abstractunlabeled
The Electronic Health Library of British Columbia (eHLbc)
Nancy Levesque
2007· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Accessible Multimedia for the Web
Nicholas E. Zaparyniuk, Jillianne Code
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
affaboutno abstractunlabeled
Video Surveillance in the Ontario Workplace
Franklin Ramsoomair, Charles Borras
2005· 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
Java-based telecollaborative environment
Abdulmotaleb El Saddik
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
The virtualMe: An integrated teaching and learning framework
Michael Verhaart, Kinshuk Kinshuk
2007· 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
Addressing Multilingual Aspects of Online Participation
Stephen Carey
2008· 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
From 0 to 6000: Creating an Attitudinal Infrastructure
Giuliana Colalillo, Sandra Hodder
2005· 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
Problem-based learning: For what learning outcomes is it working?
Johannes Ströbel, Angela van Barneveld
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
affno abstractunlabeled
Designing for a moving target
Beverly Pasian, Sandi Barber, Kathy Siedlaczek
2002· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Building Learning Webs Using Blended Learning Models
Irene Buck, Martin Buck
2006· 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
Communication Preferences and E-Learning
Heather Parker, Leonardo Ruppenthal, Mei Yee Leung, Mark Chignell
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
A neuroscientific perspective for assessing student engagement in e-learning
Patrick Charland, Pierre‐Majorique Léger, Geneviève Allaire‐Duquette, Geneviève Gingras
2013· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Neuroscience
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Applying Bloom to Games – A Preliminary Methods Description
Tracey L. Leacock, Brad Paras, Jim Bizzocchi
2004· 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
An Agent Architecture for Asynchronous Learning
Stephen Rochefort
2002· 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
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

How this was built: Screen · Findings · About