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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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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.

affaffiliation
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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 29 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
The Employment Situation, December
Murat Tasci, Beth Mowry
2008· article· en· Economic Trends· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Vocational Indecision and Return to School
Andrée LaRue, Romaine Malenfant, Mylène Jetté
2009· article· en· Savoirs· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Dolores E. Battle
Ellen Uffen
2005· article· ceb· ASHA Leader· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Lessons Learned from a Lesson Plan Database
Kévin Thomas
2004· article· en· Society for Information Technology & Teacher Education International Conference· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
What I Like About the Course
Jeff Ross
2012· article· en· Divergent/Convergent· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Why Did I Stay So Long
Randy Vlasin
2007· article· en· ˜The œAgricultural education magazine· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Monoqonuwicik Neoteetjg Mosigisig
Margaret Kress
2018· article· en· 2018 Conference of the Canadian Society for the Study of Education· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
A Global View of Curricular Leaders
Zheng Donghui
2006· article· en· Journal of Ningbo University· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Race, poverty, and teacher mobility
Benjamin Scafidi, David L. Sjoquist, Todd Stinebrickner
2005· article· en· Economics of Education Review· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About