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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 3 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
Elementary and Junior High School Use of Clickers
Norma Nocente, Greg Belostotski
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Developing a Metric for Evaluating Discussion Boards
Robin Kay
2004· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
E-Literacy for the Workforce: Designing Effective Instruction
Sonya Symons, Lisa Langille, Heather Hemming
2002· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Learning with collaborative concept maps: A Meta-Analysis
Olusola Adesope, John C. Nesbit
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Topic Tree: Increasing the Accuracy of Item Retrieval
Hicham Hage, Esma Aı̈meur
2007· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Frame Analysis Method: Monitoring Metacognitive Activities
Zekeriya Karadağ, Douglas McDougall
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
e-Learning through Experiential Learning Projects
Kalyani Premkumar, Cyril Coupal
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
E-learning Effectiveness and Culture
Hafid Agourram, Mourad Mansour
2007· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Creating OER materials - Perspectives of Global Instructors
Nathaniel Ostashewski, Serena Henderson
2017· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · open_scienceconsensus · none
1
citations
affno abstractunlabeled
Traditional and Online Forms of Education: Proposing a Common Ground
Tolulope Adesope, Olusola Adesope, Caroline Ojeme
2008· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Transnational Graduate Studies: designing the virtual seminar
Norman Vaughan, Michael Power
2009· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
A Framework for Agile Instructional Development
Sharon Bratt
2011· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Editing an Electronic Book in Virtual Team: Our Journey
Madhumita Bhattacharya, Nada Mach, Mahnaz Moallem
2011· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
Developing a Community of Videoconference Users
David Hinger
2007· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About