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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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Learning Styles and Cognitive Differences
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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.

271 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.
271 works in the cohort · of 4,299,418page 3 of 6

Labels cover 0 of 271 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 271 of 271 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.

affvenueunlabeled
Lifelong Learning
Mackenzie Robinson Graves
2018· article· en· SFU Educational Review· Psychology
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Is psychometrics science?
Geoff Norman
2016· editorial· en· Advances in Health Sciences Education· Psychology
machine prediction:candidate · metaresearchconsensus · none
6
citations
venueno affunlabeled
From Cognitive Landscapes to Digital Hyperscapes
José Bidarra, Ana Dias
2003· article· en· The International Review of Research in Open and Distributed Learning· Psychology
machine prediction:candidate · noneconsensus · none
6
citations
afffundaboutunlabeled
StrengthsQuest for Engineers
Shelley Lorimer, Elsie Elford
2020· article· en· Psychology
machine prediction:candidate · insufficient_payloadconsensus · none
3
citations
venueno affunlabeled
Learning Styles in Students of Medical Sciences
Mahnaz Shahrakipour, Azizollah Arbabisarjou, Sadegh Zare, Gholamreza Ghoreishinia
2016· article· en· Global Journal of Health Science· Psychology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Personalized Learning Systems
Sabine Graf, Kinshuk Kinshuk
2012· book-chapter· en· Psychology
machine prediction:candidate · noneconsensus · none
3
citations
venueno affunlabeled
Teaching Ways and Learning Ways Revisited
Lưu Trọng Tuấn, Nguyễn Thành Long
2010· article· en· Studies in literature and language· Psychology
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About