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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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Innovative Teaching Methods
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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.

863 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.
863 works in the cohort · of 4,299,418page 16 of 18

Labels cover 2 of 863 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 863 of 863 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.

affvenueaboutunlabeled
ACTIVE LEARNING IN A SECOND YEAR SURVEYING COURSE
Elena Rangelova, Sheng Lun Cao
2019· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Foundation Skills for Scientists: An Evolving Program
Teresa Dawson, Sarah Fedko, Nancy Johnston, Elaine Khoo, Sarah King, Saira Rachel Mall +8 more
2010· article· en· The Canadian Journal for the Scholarship of Teaching and Learning· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
0
citations
venueno affunlabeled
10.53762/rnsy9631
Muhammad Dilshad, Adnan Malik
2000· article· en· Time to knit· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Exploring the Benefits of Online Labs for On-Campus Teaching
Jennifer Van Dommelen, Martin J. Hicks, Kathleen Nolan, Donna Pattison, Ethell Vereen
2022· article· en· Advances in Biology Laboratory Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Blended Learning in First Year Engineering Labs
ANNE TOPPER, L. Clapham
2019· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Factors underpinning student perceptions of laboratory experiences
Samuel J. Priest, Simon M. Pyke
2017· article· en· Proceedings of The Australian Conference on Science and Mathematics Education (formerly UniServe Science Conference)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About