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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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Journal of Statistics Education
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Retraction
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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.

26 results · 1 filter active ·
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20002020
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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.
26 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 26 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 26 of 26 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
What Does the Mean Mean?
Nicholas Watier, Claude Lamontagne, Sylvain Chartier
2011· article· en· Journal of Statistics Education· Mathematics
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Multiple-Choice Randomization
Ian K. McLeod, Ying Zhang, Yu Hao
2003· article· en· Journal of Statistics Education· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
More on Venn Diagrams for Regression
E. Kennedy Peter
2002· article· en· Journal of Statistics Education· Mathematics
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
The Language of Conditional Probability
Jessica S. Ancker
2006· article· en· Journal of Statistics Education· Mathematics
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Fun with the R Grid Package
Lutong Zhou, W. John Braun
2010· article· en· Journal of Statistics Education· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Naive Analysis of Variance
W. John Braun
2012· article· en· Journal of Statistics Education· Mathematics
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About