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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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American Journal of Evaluation
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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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

60 results · 1 filter active ·
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20002025
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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.
60 works in the cohort · of 4,299,418page 1 of 2

Labels cover 1 of 60 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 60 of 60 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
The Evaluation of Large Research Initiatives
William M. K. Trochim, Stephen E. Marcus, Louise C. Mâsse, Richard P. Moser, Patrick C. Weld
2008· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · metaresearch
144
citations
affaboutunlabeled
A New Realistic Evaluation Analysis Method
Suzanne F. Jackson, Gillian Kolla
2012· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
144
citations
affunlabeled
Toward Accurate Measurement of Participation
Pierre‐Marc Daigneault, Steve Jacob
2009· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
86
citations
affunlabeled
Using Self-Assessments to Detect Workshop Success
Marcel D’Eon, Leslie Sadownik, Alexandra Harrison, Jill Nation
2008· article· en· American Journal of Evaluation· Psychology
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Managing Tensions Between Evaluation and Research
Lynda Rey, Marie‐Claude Tremblay, Astrid Brousselle
2013· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
44
citations
affunlabeled
Exploring the Intervention— Context Interface
Sherri Bisset, Mark Daniel, Louise Potvin
2009· article· en· American Journal of Evaluation· Health Professions
machine prediction:candidate · noneconsensus · none
33
citations
affunlabeled
Evaluating Social Innovations
Kate Svensson, Barbara Szijarto, Peter Milley, J. Bradley Cousins
2018· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Analysis of Thin Online Interview Data
Richard J. Kitto, John Barnett
2007· article· en· American Journal of Evaluation· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
13
citations
afffundunlabeled
Making Space for Adaptive Learning
Barbara Szijarto, J. Bradley Cousins
2018· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Planning for community-based evaluation
Rhonda Cockerill
2000· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Planning for Community-based Evaluation
Rhonda Cockerill, Ted Myers, Dan Allman
2000· article· en· American Journal of Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations

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