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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.

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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
Retraction
Abstract
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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
Evidence
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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 7 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.

aboutno affunlabeled
3 Reasons Chromebooks Are Shining in Education
Dian Schaffhauser
2015· article· en· T.H.E. Journal Technological Horizons in Education· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affvenueunlabeled
Portable Applications in Mobile Education
Jon Baggaley
2006· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
iPads and Their Potential to Revolutionize Learning
Camille McFarlane
2013· article· en· EdMedia: World Conference on Educational Media and Technology· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affunlabeled
IoTalho: IoT Advancing Learning from High-tech Objects
Péricles de Lima Sobreira, Jauberth Weyll Abijaude, Hellan Dellamycow Gomes Viana, Levy Marlon Souza Santiago, Karim El Guemhioui, Omar Abdul Wahab +1 more
2020· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
venueno affunlabeled
63. Mobile Learning in Developing Nations
Scott Motlik
2008· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
distilled prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Tablet Use in Higher Education
Colin F. Mang, Leslie J. Wardley
2020· book-chapter· en· Encyclopedia of Education and Information Technologies· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
3
citations
affunlabeled
Designing Mobile Learning for the User
Mohamed Ally
2012· book-chapter· en· IGI Global eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
3
citations
affunlabeled
Designing Library Services for the PDA
Dana McFarland, Jessica Mussell
2006· article· en· Journal of Library Administration· Computer Science
distilled prediction:candidate · noneconsensus · none
3
citations
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