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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 1 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.

venueno affno abstractunlabeled
How to Conduct Surveys: A Step-by-Step Guide
Lorraine Carter
2010· article· en· Canadian Journal of University Continuing Education· Decision Sciences
machine prediction:candidate · noneconsensus · none
405
citations
affunlabeled
Redefining Case Study
Rob VanWynsberghe, Samia Khan
2007· article· en· International Journal of Qualitative Methods· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
371
citations
affunlabeled
Climbing the Ladder of Research Utilization
Réjean Landry, Nabil Amara, Моктар Ламари
2001· article· en· Science Communication· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
269
citations
venueno affunlabeled
Useful Theory of Change Models
John Mayne
2015· article· en· Canadian Journal of Program Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
213
citations
fundno affunlabeled
Evaluating Impact Using Time-Series Data
Hannah S. Wauchope, Tatsuya Amano, Jonas Geldmann, Alison Johnston, Benno I. Simmons, William J. Sutherland +1 more
2020· review· en· Trends in Ecology & Evolution· Decision Sciences
machine prediction:candidate · noneconsensus · none
171
citations
affno abstractunlabeled
What types of advice do decision-makers prefer?
Reeshad S. Dalal, Silvia Bonaccio
2010· article· en· Organizational Behavior and Human Decision Processes· Decision Sciences
machine prediction:candidate · noneconsensus · none
158
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
The Politics of Policy Evaluation
Mark Bovens, Paul ‘t Hart, Sanneke Kuipers
2009· book-chapter· en· Oxford University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
136
citations
affunlabeled
Unpacking the Participatory Process
Linda M. Weaver, J. Bradley Cousins
2004· article· en· Journal of MultiDisciplinary Evaluation· Decision Sciences
machine prediction:candidate · noneconsensus · none
116
citations
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How this was built: Screen · Findings · About