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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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Educational Methods and Media Use
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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.

634 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.
634 works in the cohort · of 4,299,418page 1 of 13

Labels cover 2 of 634 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 634 of 634 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 affunlabeled
The Indonesian EFL Learners’ Motivation in Reading
Hairus Salikin, Saidna Zulfiqar Bin-Tahir, Reni Kusumaningputri, Dian Puji Yuliandari
2017· article· en· English Language Teaching· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
venueno affunlabeled
Reading Preferences of Middle School Students
Gülnur Aydın, Bilge BAĞCI AYRANCI
2018· article· en· World Journal of Education· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
aboutno affunlabeled
School libraries in South Australia 2019 Census
Katherine Dix, Rachel Felgate, Syeda Kashfee Ahmed, Toby Carslake, Shani Sniedze-Gregory
2020· report· en· Computer Science
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

How this was built: Screen · Findings · About