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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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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.

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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 3 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.

afffundunlabeled
Adoption, Improvement, and Disruption
Michael Brydon, Aidan R. Vining
2008· article· en· Journal of Database Management· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Technology Affordances: The Case of Wikipedia
Mostafa Mesgari, Samer Faraj
2012· article· en· Americas Conference on Information Systems· Computer Science
machine prediction:candidate · stsconsensus · none
23
citations
aboutno affunlabeled
The Computer Revolution in Canada
John Vardalas
2001· book· en· The MIT Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
An Empirical Study of Open Source Software Usability
Arif Raza, Luiz Fernando Capretz, Faheem Ahmed
2011· article· en· International Journal of Open Source Software and Processes· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
The sustainability of corporate wikis
Ofer Arazy, Arie Croitoru
2010· article· en· ACM Transactions on Management Information Systems· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Do Onboarding Programs Work?
Adriaan Labuschagne, Reid Holmes
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Algorithmic Interactions in Open Source Work
Maha Shaikh, Emmanuelle Vaast
2022· article· en· Information Systems Research· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
20
citations
affunlabeled
Winning the app production rally
Ehsan Noei, Daniel Alencar da Costa, Ying Zou
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
MODELING DISTRIBUTED COLLABORATION ON GITHUB
Nora McDonald, Kelly Blincoe, Eva Petakovic, Sean Goggins
2014· article· en· Advances in Complex Systems· Computer Science
machine prediction:candidate · bibliometricsconsensus · none
18
citations
affunlabeled
Open Source License Inconsistencies on GitHub
Thomas Wolter, Ann Barcomb, Dirk Riehle, Nikolay Harutyunyan
2022· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · open_scienceconsensus · none
18
citations
affunlabeled
Do onboarding programs work
Adriaan Labuschagne, Reid Holmes
2015· article· en· Mining Software Repositories· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
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