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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.

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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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Education Systems and Policy
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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.

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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.

2,366 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.
2,366 works in the cohort · of 4,299,418page 32 of 48

Labels cover 4 of 2,366 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 2,366 of 2,366 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.

aboutno affunlabeled
A plan for Melbourne's future?
Rod Duncan
2017· article· en· Planning News· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
My Wedge, My Self.
Meredith Nash
2009· article· en· Thirdspace· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Engaging the Local Community: Why Bother?
Mary Carlsen
2008· article· en· Intersections Canadian Journal of Music· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Contemporary Challenges Teaching Labour History
Mark Leier, John‐Henry Harter, Dale M. McCartney, Andrea Samoil
2019· article· en· Labour / Le Travail· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Challenges to Reform: An Overview of the Study
Roland G. Pourdavood, Lynn M. Cowen, Lawrence V. Svec
2002· article· en· Focus on learning problems in mathematics· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Am I Doing the Right Thing
Ellen Rodger
2001· article· en· Hecate· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Empowering Learners - Changing the World
Justine Bizzocchi
2005· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Embracing Family Literacy.
Deborah L. Wolter
2000· article· en· Early childhood education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Career Development Practice: A Renewal and Focus.
Wendy Patton, Mary McMahon
2001· book-chapter· en· Australian Council for Educational Research eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Education reform initiatives in Canada
Richard M. Jones
2008· article· en· Medical Entomology and Zoology· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Education, Globalization and Skill
Phillip Brown, Hugh Lauder, David Ashton
2008· book-chapter· en· ORCA Online Research @Cardiff (Cardiff University)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
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