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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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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
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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.

3,084 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.
3,084 works in the cohort · of 4,299,418page 38 of 62

Labels cover 10 of 3,084 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 3,084 of 3,084 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.

afffundunlabeled
Dialectic Preference Bias in Large Language Models
Muhammad Hassan, Faiza Khan Khattak, Laleh Seyyed-Kalantari
2025· article· en· Proceedings of the AAAI Symposium Series· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affvenueunlabeled
Uses of Word Embeddings in Engineering Education
Tamara Kecman, Susan McCahan
2024· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A personal view of APL
Kenneth E. Iverson
2000· article· en· ACM SIGAPL APL Quote Quad· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Replication Data for: Multi-label Prediction for Political Text-as-Data
Aaron Erlich, Stefano G. Dantas, Benjamin E. Bagozzi, Daniel Berliner, Brian Palmer‐Rubin
2021· dataset· en· London School of Economics and Political Science Theses Online (London School of Economics and Political Science)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Similarity patterns in words
Grzegorz Kondrak
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Bilingual Corpora
Caroline Barrière
2016· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A Written Child Corpus with Editing History Tags
Ryo Nagata, Ayako Kawai, Koji Suda, Junichi Kakegawa, Koichiro Morihiro
2010· article· en· Journal of Natural Language Processing· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
fundno affno abstractunlabeled
The Evolution of the Language Laboratory
Edward M. Stack
2019· article· en· IALLT Journal of Language Learning Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About