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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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Research and Practice in Technology Enhanced 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
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.

27 results · 1 filter active ·
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20062025
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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.
27 works in the cohort · of 4,299,418page 1 of 1

Labels cover 1 of 27 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 27 of 27 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.

affunlabeled
Context or culture: what is the difference?
Isabelle Savard, Riichiro Mizoguchi
2019· article· en· Research and Practice in Technology Enhanced Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
The effectiveness of using in-game cards as reward
Peayton Chen, Rita Kuo, Maiga Chang, Jia-Sheng Heh
2017· article· en· Research and Practice in Technology Enhanced Learning· Psychology
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Emerging trends for open access learning
Rachid Benlamri, Fanny Klett
2015· editorial· en· Research and Practice in Technology Enhanced Learning· Computer Science
machine prediction:candidate · scholarly_communication+open_scienceconsensus · none
4
citations
fundno affunlabeled
PECUNIA - A LIFE SIMULATION GAME FOR FINANCE EDUCATION
David A. Jones, Maiga Chang, Kinshuk
2022· article· en· Research and Practice in Technology Enhanced Learning· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About