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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 2 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.

afffundunlabeled
Context-Aware Services for Smart Learning Spaces
Kristopher Scott, Rachid Benlamri
2010· article· en· IEEE Transactions on Learning Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
76
citations
affno abstractunlabeled
The Future of Ubiquitous Learning
Marcelo Fabián Maina, Kinshuk Kinshuk
2015· book· en· Lecture notes in educational technology· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
venueno affunlabeled
The Use of Social Media in E-Learning: A Metasynthesis
Ernest Mnkandla, Ansie Minnaar
2017· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
aboutno affunlabeled
The Status of Ubiquitous Computing
David G. Brown, Karen R. Petitto
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
67
citations
affunlabeled
Merging MOOC and mLearning for Increased Learner Interactions
Inge de Waard, Apostolos Koutropoulos, Rebecca J. Hogue, Sean C. Abajian, Nilgün Özdamar, C. Osvaldo Rodriguez +1 more
2012· article· en· International Journal of Mobile and Blended Learning· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
affaboutunlabeled
Canadian academic libraries and the mobile web
Robin Canuel, Chad Crichton
2011· article· en· New Library World· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
54
citations
afffundunlabeled
Context-aware recommender for mobile learners
Rachid Benlamri, Xiaoyun Zhang
2014· article· en· Human-centric Computing and Information Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations

How this was built: Screen · Findings · About