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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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Evaluation and Performance Assessment
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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.

2,407 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.
2,407 works in the cohort · of 4,299,418page 28 of 49

Labels cover 84 of 2,407 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 2,407 of 2,407 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.

affno abstractunlabeled
Evaluation and Policy Evaluation
Steve Jacob
2023· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Learning from Mistakes
Hassan Qudrat‐Ullah
2025· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutgpt · no categorygrok · no categoryopus · metaresearchmodels split
Using Rubrics for an Evaluation: A National Research Council Pilot
Ghislaine Tremblay, Melissa Fraser, Frédèric Bertrand
2017· article· en· Canadian Journal of Program Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Commissions of inquiry and policy analysis
Carolyn Johns, Gregory J. Inwood
2018· book-chapter· en· Policy Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Issue Information
2018· paratext· en· European Journal of Education· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
aboutno affunlabeled
Public Impact-Focused Research Survey Results
Kevin Gardner, Scott Slovic, Terri Goss-Kinzy, Christopher Keane
2019· report· en· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
1
citations
affno abstractunlabeled
Developing a Metric for Evaluating Discussion Boards
Robin Kay
2004· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
Evaluate This! A Case for Developing Evaluation Competencies
Linzi Williamson, Daniel W. Robertson, Kirstian Gibson, Micheal Heimlick, Sarah L. Sangster, Karen Lawson
2016· article· en· Canadian Journal of Program Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
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