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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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Experimental Learning in Engineering
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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.

892 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.
892 works in the cohort · of 4,299,418page 8 of 18

Labels cover 1 of 892 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 892 of 892 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.

afffundvenueaboutunlabeled
Enhanced remote laboratory work for engineering training
Maarouf Saad, Radhi Mhiri, Moustapha Dodo Amadou, Sandra Sahli, Saber Ouertani, Gérald Brady +1 more
2013· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Software Suite for Self-Paced Learning
Meng Zhang, Zi Xu Gu, Adan Amer, Gaganpreet Sidhu, Seshasai Srinivasan
2022· article· en· International Journal of Emerging Technologies in Learning (iJET)· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
afffundvenueunlabeled
The Teaching Laboratory Data Management (TLDM) System
Derek Yau Chung Choy, Jim Sibley, D. C. W. Kannangara
2019· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Beyond Angry Birds™
Isha DeCoito, Tasha Richardson
2017· book-chapter· en· Advances in educational technologies and instructional design book series· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
On Local Testability in the Non-Signaling Setting
Alessandro Chiesa, Peter Manohar, Igor Shinkar
2020· article· en· DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
afffundvenueunlabeled
UNDERSTANDING MENTOR NEEDS IN ONLINE ENGINEERING OUTREACH
Katherine Dornian, Mohammad Moshirpour, Laleh Behjat
2021· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Engineering
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About