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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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The International Review of Research in Open and Distributed Learning
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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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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,490 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.
1,490 works in the cohort · of 4,299,418page 18 of 30

Labels cover 4 of 1,490 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 1,490 of 1,490 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
Distance and blended learning in Asia
Tony Bates
2010· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
14
citations
venueno affunlabeled
Are K–12 Teachers Ready for E-learning?
Elif Polat, Sinan Hopcan, Ömer Yahşi
2022· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
14
citations
venueno affunlabeled
Footprints of emergence
Roy Williams, Jenny Mackness, Simone Gumtau
2012· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
14
citations
affvenueunlabeled
Troubleshooters for Tasks of Introductory Programming MOOCs
Marina Lepp, Tauno Palts, Piret Luik, Kaspar Papli, Reelika Suviste, Merilin Säde +3 more
2018· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · noneconsensus · none
14
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
venueno affunlabeled
Striving Toward Openness: But What Do We Really Mean?
Vivien Rolfe
2017· article· en· The International Review of Research in Open and Distributed Learning· Computer Science
machine prediction:candidate · open_scienceconsensus · none
14
citations
venueno affunlabeled
Distance Education and the Open University of Brazil
Welinton Baxto, Rosana Amaro, João Mattar
2019· article· en· The International Review of Research in Open and Distributed Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
14
citations
venueno affunlabeled
Time Shifting and Agile Time Boxes in Course Design
Anders Norberg, Birgit Stöckel, Marta‐Lena Antti
2017· article· en· The International Review of Research in Open and Distributed Learning· Psychology
machine prediction:candidate · noneconsensus · none
14
citations

How this was built: Screen · Findings · About