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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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Open Source Software Innovations
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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.

725 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.
725 works in the cohort · of 4,299,418page 13 of 15

Labels cover 4 of 725 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 725 of 725 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
eCampusAlberta
Tricia Donovan, Janet Paterson-Weir
2013· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Correction to: Examining ownership models in software teams
Umme Ayman Koana, Quang Hy Le, Shaikur Raman, Chris Carlson, Francis Chew, Maleknaz Nayebi
2024· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
The Human Factor in Open Source
Cat Allman
2009· article· en· ˜The œopen source business resource· Computer Science
machine prediction:candidate · open_scienceconsensus · none
0
citations
venueno affunlabeled
Crowdsourcing Practices in Academic Libraries in Nigeria
Jacob Kehinde Opele, Cecilia Funmilayo Daramola, Glory Onoyeyan
2025· article· en· Evidence Based Library and Information Practice· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
0
citations
affunlabeled
Intellectual Property Systems in Software
Ricardo J. Rejas‐Muslera, Elena Davara, Alain Abran, Luigi Buglione
2012· book-chapter· en· Digital Rights Management· Computer Science
machine prediction:candidate · stsconsensus · none
0
citations
affunlabeled
Robo Ludens: A Playful Conception of the Social
Ceyda Yolgörmez
2025· book-chapter· en· Frontiers in artificial intelligence and applications· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Open Source Development and Licensing
Steven J. Henry
2004· other· en· The Internet Encyclopedia· Computer Science
machine prediction:candidate · open_scienceconsensus · none
0
citations
affvenueunlabeled
Vendre quoi à qui?
Hélène Sicotte, Line Ricard, Claudia Rebolledo, Mario Bourgault
2003· article· fr· Gestion· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Open Source in Canada's Public Sector
2008· article· en· DOAJ (DOAJ: Directory of Open Access Journals)· Computer Science
machine prediction:candidate · open_scienceconsensus · none
0
citations
affvenueunlabeled
Commercial Internet Publishing--The Practicalities
John C. Nash, Mary Nash
2013· article· en· Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About