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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
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 6 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.

affvenueunlabeled
Redesigning for mobile plurilingual futures
Heather Lotherington, Kurt Thumlert, Taylor Boreland, Brittany Tomin
2022· article· en· OLBI Journal· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
fundno affunlabeled
AmbiLearn
Jennifer Hyndman, Tom Lunney, Paul Mc Kevitt
2011· article· en· International Journal of Ambient Computing and Intelligence· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
venueno affunlabeled
Smartphones and their role in the modern classroom
Tamilla Mammadova
2018· article· en· Revue internationale des technologies en pédagogie universitaire· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Developing a Mobile Learning Maturity Model
Muasaad Alrasheedi, Luiz Fernando Capretz
2013· article· en· International Journal for Infonomics· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Practical Issues in Mobile Education
Marguerite Koole
2006· article· en· AUSpace (Athabasca University)· Computer Science
distilled prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Applying CMM towards an m-learning context
Muasaad Alrasheedi, Luiz Fernando Capretz
2013· article· en· International Conference on Information Society· Computer Science
distilled prediction:candidate · scholarly_communication+insufficient_payloadconsensus · insufficient_payload
6
citations
affno abstractunlabeled
Transactions on Edutainment III
Zhigeng Pan, Maiga Chang, Adrian David Cheok
2009· book· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
6
citations
affunlabeled
Context-aware learning path planner
Maiga Chang, Alex R. Chang, Jia‐Sheng Heh, Tzu‐Chien Liu
2008· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
aboutno affunlabeled
Project Management: Going the Distance.
Cecilia Hannigan, Michael Browne
2000· article· en· International journal of instructional media· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations

How this was built: Screen · Findings · About