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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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E-Learning and Knowledge Management
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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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venuejournal
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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.

345 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.
345 works in the cohort · of 4,299,418page 2 of 7

Labels cover 0 of 345 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 345 of 345 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 affunlabeled
Videoconference in Academic Tutoring: A Case Study
Fátima Margarita Castro León, José Ma del Castillo-Olivares Barberán, David Pérez‐Jorge, Juan José Leiva Olivenza
2018· article· en· Asian Social Science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Client sponsored projects in software engineering courses
Williams C. Judith, Bettina Bair, Jürgen Börstler, Timothy C. Lethbridge, Ken Surendran
2003· article· en· ACM SIGCSE Bulletin· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
6
citations
venueno affunlabeled
Abrir la ciencia para cambiar el mundo
Antonio Lafuente
2020· article· es· International Journal of Engineering Social Justice and Peace· Computer Science
machine prediction:candidate · open_scienceconsensus · none
5
citations
fundno affunlabeled
Repositorio de Recursos Educativos Abiertos: Un caso práctico
Gloria Concepción Tenorio Sepúlveda, Magally Martínez Reyes, Anabelem Soberanes Martín
2019· article· es· CPU-e Revista de Investigación Educativa· Computer Science
machine prediction:candidate · open_scienceconsensus · none
5
citations
venueno affunlabeled
ARGUMENTATION IN ENGINEERING EDUCATION
Adriano J. Garcia, Tarso Bonilha Mazzotti
2017· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Modeling for Learning
Josianne Basque, Béatrice Pudelko
2010· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
L’Éducation à Distance à l’UNAM
Rosario Freixas Flores, Fernando Gamboa Rodríguez
2016· article· fr· Distances et médiations des savoirs· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About