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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 6 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
Back to Basics: Hybrid Learning and Comfortable Computing.
Raj Boora
2004· 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
Extending the E-Learning Experience by Using Mobile Technology
Kathryn Mac Callum, Lynn Jeffrey, 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
From the Global to the Local: New Policies for New Technologies
Brian Lewis, Jennifer Jenson, Richard Smith
2002· 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
0
citations
affno abstractunlabeled
A Note On Language-Centered eLearning Analytics
Noureddine Elouazizi
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
How do readers respond to social text signals
Andrew Chiarella
2011· 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
Electronic Learning Modules for Human Physiology
Kirk Hillier
2011· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Supporting a Blended Learning Course by Podcasting
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
affno abstractunlabeled
Using Tools to Enhance Achievement in Online Study
Rob McTavish
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
affno abstractunlabeled
The Veltic Hypermedia System: A Solution for Sharing Best Practices Online
Hélène Fournier, Claire IsaBelle, Rodrigue Savoie, Phyllis Dalley, François Desjardins
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
Evaluation of a High-end Distance Delivery MBA Program
D. R. Dicks
2002· 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
0
citations
aboutno affno abstractunlabeled
E-Learning in British Columbia
Justine Bizzocchi
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
A Systematic Comparative Analysis of MOOC Participant Profiles
Bruno Poëllhuber, Normand Roy, Ibtihel Bouchoucha
2015· 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
E-Learning that Adapts to the Learner
Jutta Treviranus
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

How this was built: Screen · Findings · About