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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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Competitive and Knowledge Intelligence
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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.

371 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
371 works in the cohort · of 4,299,418page 4 of 8

Labels cover 1 of 371 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 371 of 371 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.

affvenueaboutunlabeled
Environmental Scanning by Canadian CEOs
Chun Wei Choo
2013· article· en· Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Historical Perspective
M. Gordon Hunter
2007· book-chapter· en· IGI Global eBooks· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
How We Got Here
André Turcotte
2020· book-chapter· en· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Michael Fernandes at Nicholas Piramal (TN)
Michel Anteby, Nitin Nohria
2007· article· en· SSRN Electronic Journal· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
The Structure and Dynamics of Organizational Knowledge
Chun Wei Choo, Brian Detlor, Don Turnbull
2000· book-chapter· en· Information science and knowledge management· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Intuition in Organizations: Research and Practice
Çinla Akinci, Marta Sinclair
2018· article· en· Academy of Management Proceedings· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Conclusion and Future Directions
Kimiz Dalkir, Susan McIntyre
2014· book-chapter· en· Advances in human resources management and organizational development book series· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
The Executive Information Portal: An Extended Abstract
John Mylopoulos, Attila Barta, Raoul Jarvis, Patricia Rodríguez-Gianolli, Shun Zhou
2001· article· en· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Information in Times of Crisis: Learning Together
Luanne Sinnamon, Rachael Huegerich
2025· article· en· SSRN Electronic Journal· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations
0
citations

How this was built: Screen · Findings · About