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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.

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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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Ubiquity Press eBooks
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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.

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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.

22 results · 1 filter active ·
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20122024
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Categories
Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
22 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 22 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 22 of 22 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
Introduction to Open
Robert Biswas‐Diener, Rajiv S. Jhangiani
2017· book-chapter· en· Ubiquity Press eBooks· Computer Science
machine prediction:candidate · open_science+insufficient_payloadconsensus · none
18
citations
fundno affunlabeled
Conversations with Kenelm
John Took
2013· book· en· Ubiquity Press eBooks· Arts and Humanities
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
How to Open an Academic Department
Farhad N. Dastur
2017· book-chapter· en· Ubiquity Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Monitoring and evaluating EPAs and EPA frameworks
David Taylor, Alan Schwartz, Abigail T. Elmes, Adrian Marty, Marije P. Hennus
2024· book-chapter· en· Ubiquity Press eBooks· Environmental Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Integrating SSR and SALW Programming
Mark Sedra, Geoff Burt
2016· book· en· Ubiquity Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Gauteng
Johan W. Joubert
2016· book-chapter· en· Ubiquity Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Toronto
Adam Weiss, Peter Kucireck
2016· book-chapter· en· Ubiquity Press eBooks· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Animating Past Worlds
Michael Carter
2021· book-chapter· en· Ubiquity Press eBooks· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The role of simulation in EPA-based curricula
Timo de Raad, Fremen Chihchen Chou, Adrian Marty, Ryan Brydges
2024· book-chapter· en· Ubiquity Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Advancing and sustaining excellence in EPA-based curricula
Machelle Linsenmeyer, Andrew K. Hall, Chien‐Yu Chen, María José López, Fremen Chihchen Chou
2024· book-chapter· en· Ubiquity Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About