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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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Innovative Approaches in Technology and Social Development
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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.

989 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.
989 works in the cohort · of 4,299,418page 19 of 20

Labels cover 5 of 989 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 989 of 989 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 affno abstractunlabeled
Toronto: il cluster multimediale
2004· book-chapter· it· IRIS - Institutional Research Information System (Libera Università Internazionale degli Studi Sociali Guido Carli)· Business, Management and Accounting
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
I. SUMMARY
2003· article· en· Business, Management and Accounting
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Commercialization of technology research for benefit
M. Kathryn Brohman, Paul A. S. Ward
2015· article· en· Computer Science and Software Engineering· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
A Guide to Inter-Indigenous Co-Labbing
Mylène Yannick Gamache, Adrienne Huard, Nicole Stonyk, A. E. D. Daniels, Hope Ace
2025· article· en· American Indian Culture and Research Journal· Business, Management and Accounting
machine prediction:candidate · stsconsensus · none
0
citations
aboutno affunlabeled
Restitution du programme de recherche FORAVIQ
Alice Rouyer, Marina Casula
2015· other· fr· Toulouse Capitole Publications (University Toulouse 1 Capitole)· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Hearables: eLearning in the Workplace
Rory McGreal
2019· article· en· EDEN Conference Proceedings· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Editorial: DRS2022 Labs
Juan Sádaba
2022· editorial· en· Proceedings of DRS· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
How Service Innovation Boosts Bottom Lines
Claude Legrand, Rob LaJoie
2013· article· en· Technology Innovation Management Review· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
“Do-It-Together” and Alternative Innovations
Laurent Dupont, Fédoua Kasmi, Joshua M. Pearce, J. Roland Ortt
2023· other· en· HAL (Le Centre pour la Communication Scientifique Directe)· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About