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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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Teacher librarian
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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.

118 results · 1 filter active ·
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20002019
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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.
118 works in the cohort · of 4,299,418page 1 of 3

Labels cover 3 of 118 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 118 of 118 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.

venueno affno abstractunlabeled
Reading the Internet.
Keith McPherson
2005· article· en· Teacher librarian· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
15
citations
venueno affno abstractunlabeled
Make Your Load Lighter with STARS.
Barbara Braxton
2005· article· en· Teacher librarian· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
12
citations
venueno affno abstractunlabeled
Collaboration: Where Does It Begin?.
Ruth V. Small
2002· article· en· Teacher librarian· Social Sciences
machine prediction:candidate · noneconsensus · none
12
citations
venueno affno abstractunlabeled
Making the Case for Pleasure Reading.
Colleen MacDonell
2004· article· en· Teacher librarian· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
venueno affno abstractunlabeled
Inventory: 26 Reasons for Doing One.
Barbara Braxton
2005· article· en· Teacher librarian· Psychology
machine prediction:candidate · noneconsensus · none
9
citations
venueno affno abstractunlabeled
Become a Google Power User
Holly Gunn
2005· article· en· Teacher librarian· Business, Management and Accounting
machine prediction:candidate · insufficient_payloadconsensus · none
8
citations
venueno affno abstractunlabeled
Visual Literacy and School Libraries.
Keith McPherson
2004· article· en· Teacher librarian· Arts and Humanities
machine prediction:candidate · noneconsensus · none
7
citations
venueno affno abstractunlabeled
Connecting with Aboriginal Students.
Diane Gallagher-Hayashi
2004· article· en· Teacher librarian· Social Sciences
machine prediction:candidate · noneconsensus · none
6
citations
venueno affno abstractunlabeled
Effective Roles for Student Achievement.
Ken Haycock
2004· article· en· Teacher librarian· Social Sciences
machine prediction:candidate · noneconsensus · none
5
citations
venueno affno abstractunlabeled
Bring Back the Poetry.
Betsy Bryan Miguez
2005· article· en· Teacher librarian· Arts and Humanities
machine prediction:candidate · noneconsensus · none
5
citations
venueno affno abstractunlabeled
Getting Students to Write Using Comics.
Mark Crilley
2009· article· en· Teacher librarian· Arts and Humanities
machine prediction:candidate · noneconsensus · none
5
citations
venueno affno abstractunlabeled
Online Material Selection.
Shonda Brisco
2004· article· en· Teacher librarian· Materials Science
machine prediction:candidate · insufficient_payloadconsensus · none
4
citations
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How this was built: Screen · Findings · About