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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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Language learning and language teaching
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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.

35 results · 1 filter active ·
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20062024
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
35 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 35 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 35 of 35 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.

affunlabeled
Pronunciation Fundamentals
Tracey M. Derwing, Murray J. Munro
2015· book· en· Language learning and language teaching· Psychology
machine prediction:candidate · noneconsensus · none
606
citations
aboutno affunlabeled
Goals for Academic Writing
Alister Cumming
2006· book· en· Language learning and language teaching· Arts and Humanities
machine prediction:candidate · noneconsensus · none
85
citations
aboutno affunlabeled
Studies in French Applied Linguistics
Dalila Ayoun
2008· book· en· Language learning and language teaching· Social Sciences
machine prediction:candidate · noneconsensus · none
37
citations
affunlabeled
The COLT Observation Scheme
Nina Spada
2024· book· en· Language learning and language teaching· Psychology
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Discussion
Nina Spada
2019· book-chapter· en· Language learning and language teaching· Arts and Humanities
machine prediction:candidate · insufficient_payloadconsensus · none
14
citations
affaboutunlabeled
Chapter 5. When three equals tree
Pavel Trofimovich, Paul John
2011· book-chapter· en· Language learning and language teaching· Psychology
machine prediction:candidate · noneconsensus · none
7
citations
aboutno affunlabeled
11. Language planning and policy in Quebec
Leigh Oakes
2008· book-chapter· fr· Language learning and language teaching· Social Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Chapter 7. The L2 semantic attentional blink
Norman Segalowitz, Guy Lacroix, Jenelle Job
2011· book-chapter· en· Language learning and language teaching· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Chapter 6. Assessment of L2 pronunciation
Tracey M. Derwing, Murray J. Munro
2015· book-chapter· en· Language learning and language teaching· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Chapter 2. Historical overview of pronunciation
Tracey M. Derwing, Murray J. Munro
2015· book-chapter· en· Language learning and language teaching· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Talking to self while writing
Yuko Watanabe
2020· book-chapter· en· Language learning and language teaching· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About