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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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Ethics in Business and Education
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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.

1,037 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.
1,037 works in the cohort · of 4,299,418page 19 of 21

Labels cover 9 of 1,037 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 1,037 of 1,037 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
Business Ethics Competencies
David Cramm, Ronel Erwee
2015· book-chapter· en· Advances in business strategy and competitive advantage book series· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Warning Lights on the Dashboard
Ian Burt, Sally Gunz, John McCutcheon
2014· article· en· Accounting Perspectives· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Is ethics evaporating in the cyber era?
Alfredo M. Ronchi
2022· article· en· The International Review of Information Ethics· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Business Ethics Competencies
David Cramm, Ronel Erwee
2015· book-chapter· en· IGI Global eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
The Ethics of Labour-Management Relations
Goetz A. Briefs
2014· article· en· Relations industrielles· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Ethical Blind Spots and Accounting
Krista Fiolleau
2018· book-chapter· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Moral Imagination for Engineering Teams: The Technomoral Scenario
Geoff Keeling, Benjamin Lange, Amanda McCroskery, Kyle Pedersen, David Weinberger, Ben Zevenbergen
2024· article· en· The International Review of Information Ethics· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Conflito ético na comunicação: greenwashing
Alexandre De Souza, Fabrício Ricardo Perrella, Gisele Angela Tartaro Ho, Manuel Fernandes Silva Souza, Sí­lvio Augusto Minciotti
2023· article· pt· Cuadernos de Educación y Desarrollo· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Human Resource Management from a Justice-Based Perspective
Tahir Masood Qureshi, Mohemmed Absuweilem, Shareefa Reda Alkhateeb, Verl Anderson, Cam Caldwell
2020· article· en· Business and Management Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About