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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
aboutaboutness

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 8 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.

affunlabeled
Theory-Driven Entrepreneurial Search
Ankur Chavda, Joshua S. Gans, Scott Stern
2024· report· en· National Bureau of Economic Research· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Jamii: A Virtual Incubation Platform for Entrepreneurs
Howard M. Armitage, Catherine Bischoff, Karin Schmidlin, Douglas Sparkes
2018· article· en· Journal of International Business Research and Marketing· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Value-Risk Analysis of Crowdsourcing inPakistan’s Perspective
Muhammad Saady, Qurat- ul-Ain, Sidra Anwar, Sadia Anayat, Samia Rafique
2021· article· en· International Journal of Information Engineering and Electronic Business· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
venueno affno abstractunlabeled
Licensing of Open APIs
G. R. Gangadharan
2009· article· en· ˜The œopen source business resource· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Managing Knowledge in Open Source Software Test Process
Tamer Abdou, Peter Grogono, Pankaj Kamthan
2013· book-chapter· en· Advances in systems analysis, software engineering, and high performance computing book series· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Certification of Open Source Software – A Scoping Review
Eirini Kalliamvakou, Jens Weber, Alessia Knauss
2016· review· en· IFIP advances in information and communication technology· Computer Science
machine prediction:candidate · open_scienceconsensus · none
2
citations
affunlabeled
Learning From Doing
Nicole Wang, Martin K.‐C. Yeh, William C. Diehl, Rebecca E. Heiser, Andrea Gregg, Ling Tran +1 more
2021· article· en· International Journal of Web-Based Learning and Teaching Technologies· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Open Source Software: Free Isn't Exactly Cheap!
Ben A. Calloni, John F. McGowan, Randall Stanley
2005· article· en· Infotech@Aerospace· Computer Science
machine prediction:candidate · open_scienceconsensus · none
2
citations

How this was built: Screen · Findings · About