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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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Educational Games and Gamification
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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 2 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
Gamification of Entrepreneurship Education
Diane A. Isabelle
2020· article· en· Decision Sciences Journal of Innovative Education· Psychology
machine prediction:candidate · noneconsensus · none
102
citations
affunlabeled
Beyond designing for motivation
Chad Richards, Craig W. Thompson, Nicholas Graham
2014· article· en· Psychology
machine prediction:candidate · noneconsensus · none
100
citations
venueno affunlabeled
Archetypes of Gamification: Analysis of mHealth Apps
Manuel Schmidt-Kraepelin, Philipp A Toussaint, Scott Thiebes, Juho Hamari, Ali Sunyaev
2020· article· en· JMIR mhealth and uhealth· Psychology
machine prediction:candidate · noneconsensus · none
87
citations
affunlabeled
Efficacy of Serious Games in Healthcare Professions Education
Marc‐André Maheu‐Cadotte, Sylvie Cossette, Véronique Dubé, Guillaume Fontaine, Andréane Lavallée, Patrick Lavoie +2 more
2020· review· en· Simulation in Healthcare The Journal of the Society for Simulation in Healthcare· Psychology
machine prediction:candidate · noneconsensus · none
82
citations
affunlabeled
Pedagogy in Commercial Video Games
Katrin Becker
2006· book-chapter· en· Open MIND· Psychology
machine prediction:candidate · noneconsensus · none
75
citations
affno abstractunlabeled
Pokémon Go and Research
Alexander M. Clark, Matthew Clark
2016· article· en· International Journal of Qualitative Methods· Psychology
machine prediction:candidate · noneconsensus · none
70
citations
affunlabeled
Metabolic Requirements of Interactive Video Game Cycling
Darren E. R. Warburton, Daniel Sarkany, Mika Johnson, Ryan E. Rhodes, WARREN WHITFORD, Ben T. Esch +3 more
2009· article· en· Medicine & Science in Sports & Exercise· Psychology
machine prediction:candidate · noneconsensus · none
67
citations
afffundunlabeled
Time's up
João P. Costa, Rina R. Wehbe, James Robb, Lennart E. Nacke
2013· article· en· Psychology
machine prediction:candidate · noneconsensus · none
62
citations

How this was built: Screen · Findings · About