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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
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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
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venuejournal
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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.

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 4 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. 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.

venueno affunlabeled
Using Tablet on Education
Reteeba Algoufi
2016· article· en· World Journal of Education· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
The HCI living curriculum as a community of practice
Olivier St-Cyr, Andrea Jovanovic, Mark Chignell, Craig M. MacDonald, Elizabeth F. Churchill
2018· article· en· interactions· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affaboutunlabeled
International Perspectives on Literacy Learning with iPads
Tiffany L. Gallagher, Douglas Fsher, Diane Lapp, Jennifer Rowsell, Alyson Simpson, Ruth McQuirter +3 more
2015· article· en· Journal of Education· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Ubiquitous Learning
Kinshuk Kinshuk, Sabine Graf
2012· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
venueno affunlabeled
Mobile Technology
Evan Michael Fox
2019· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Mobile Technologies in Teacher Education
Jan Herrington, Nathaniel Ostashewski, Doug Reid, Kim Flintoff
2014· book-chapter· en· SensePublishers eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
13
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
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
What’s a Cellphilm? An Introduction
Katie MacEntee, Casey Burkholder, Joshua Schwab-Cartas
2016· book-chapter· de· SensePublishers eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
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