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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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Mobile Learning in 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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

690 results · 1 filter active ·
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20002025
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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.
690 works in the cohort · of 4,299,418page 5 of 14

Labels cover 2 of 690 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 690 of 690 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
What’s a Cellphilm? An Introduction
Katie MacEntee, Casey Burkholder, Joshua Schwab-Cartas
2016· book-chapter· de· SensePublishers eBooks· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
12
citations
affunlabeled
"Showing off" your mobile device
Cosmin Munteanu, Heather Molyneaux, Daniel McDonald, Jo Lumsden, Rock Leung, Hélène Fournier +1 more
2011· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
12
citations
affno abstractunlabeled
Current m-learning Research: A Review of Key Literature
Ryan Ali, Valerie Irvine
2009· review· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Computer Science
distilled prediction:candidate · metaepi_narrow+research_integrityconsensus · none
12
citations
affunlabeled
Factors Influencing Students
Selvi Roy
2018· article· en· 2018 Conference of the Canadian Society for the Study of Education· Computer Science
distilled prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
Models for Interdisciplinary Mobile Learning
2011· book· en· Advances in mobile and distance learning book series· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
9
citations
venueno affno abstractunlabeled
Inkboard–Tablet PC Enabled Design-Oriented Learning
John Williams, Alexander H. Slocum, Abel Sanchez
2005· article· en· Advanced Technology for Learning· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
9
citations
affno abstractunlabeled
Design Principles for Making Meaningful Mathematics Apps
Timothy Pelton, Leslee Francis Pelton
2011· article· en· Society for Information Technology & Teacher Education International Conference· Computer Science
distilled prediction:candidate · noneconsensus · none
9
citations
aboutno affunlabeled
Information Literacy gets mobile in Vancouver
Peter Godwin
2009· article· en· Journal of Information Literacy· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
9
citations
affunlabeled
ClassApp: A Motivational Course-level App
Fidelia A. Orji, Ralph Deters, Jim Greer, Julita Vassileva
2018· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
9
citations

How this was built: Screen · Findings · About