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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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Educational Games and Gamification
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.

affaffiliation
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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.

1,648 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.
1,648 works in the cohort · of 4,299,418page 33 of 33

Labels cover 5 of 1,648 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 1,648 of 1,648 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
Go with the flow
Claire Hewson
2017· article· en· Practical Pre-School· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Des évaluations éducatives par le jeu
Jack Buckley, Laura Colosimo, Rebecca Kantar, Marty McCall, Erica L. Snow
2022· book-chapter· fr· OECD eBooks· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
eInclusion by means of Digital Game-Based Learning
Maja Pivec, Olga Dziabenko, Paul Kearney
2005· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
EC154 Port St Johns - S71 Q2 - 4 February 2013
2013· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Psychology
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Resident-driven peer simulation curriculum
Kyle Murphy, Joshua Gould, Natalie Bandrauk
2017· article· en· The Journal of Macrodynamic Analysis (Memorial University of Newfoundland)· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
DEEP DIVE:Principles of Game-Based Learning
Sandra Davidson
2020· article· en· Nursing Education Research Conference 2020· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Hunting the elusive tiger
Wolff‐Michael Roth
2013· book-chapter· en· SensePublishers eBooks· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
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