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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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Intelligent Tutoring Systems and Adaptive Learning
Retraction
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Label agreement
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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.

789 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.
789 works in the cohort · of 4,299,418page 7 of 16

Labels cover 1 of 789 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 789 of 789 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
Working Memory and Language
John W. Schwieter, Zhisheng Wen, Teresa Bennett
2022· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Artificial Intelligence: The Views of Tertiary-Level Foreign Language Learners
Mariane Gazaille, Dana Di Pardo Léon-Henri, Andréanne L. Nolin, Noémie Gendron Perrault
2022· book-chapter· en· Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Linear models of student skills for static data.
Michel C. Desmarais, Rhouma Naceur, Behzad Beheshti
2012· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
STI-DICO
Alexandra Sasha Luccioni, Roger Nkambou, Jean Massardi, Jacqueline Bourdeau, Claude Coulombe
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
ITS in Ill-Defined Domains: Toward Hybrid Approaches
Philippe Fournier‐Viger, Roger Nkambou, Engelbert Mephu Nguifo, André Mayers
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
An Emotional Student Model for Game-Based Learning
Karla Muñoz Esquivel, Paul Mc Kevitt, Tom Lunney, Julieta Noguez, Luis Neri
2012· book-chapter· en· Advances in educational technologies and instructional design book series· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
AURELLIO
Mehdi Najjar, André Mayers
2007· article· en· International Journal of Cognitive Informatics and Natural Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About