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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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Computational and Text Analysis Methods
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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.

514 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.
514 works in the cohort · of 4,299,418page 6 of 11

Labels cover 5 of 514 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 514 of 514 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.

aboutno affunlabeled
Ramabai Espinet
2014· other· es· University of Minnesota Digital Conservancy (University of Minnesota)· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Surco - 2017/07/07 Atomic Cowboy Bootleg
2017· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Learning Unsupervised Representations from Biomedical Text
Christopher Meaney, Karen Tu, Liisa Jaakkimainen, Michael Escobar, Frank Rudzicz, Jessica Widdifield
2018· article· en· International Journal for Population Data Science· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Study2_Analysis.do
Regina Bateson
2020· dataset· en· Harvard Dataverse· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Obituary: Yorick Wilks
John Tait, Robert Gaizauskas, Kalina Bontcheva
2023· article· en· Computational Linguistics· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
H&G-Rep.csv
John S. Ahlquist, Christian Breunig
2011· dataset· en· Harvard Dataverse· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Computational Social Science
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Textual Analysis
Paul A. Fortier
2006· book-chapter· en· University of Calgary Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Parents in Parliament Dataset
2021· other· en· OSF Preprints (OSF Preprints)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About