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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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Crime, Illicit Activities, and Governance
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

940 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
Language
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
940 works in the cohort · of 4,299,418page 18 of 19

Labels cover 1 of 940 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 940 of 940 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.

venueno affno abstractunlabeled
Theft and Retribution
Beverly Watson
2007· article· en· Canadian women's studies· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Correction to: how pervasive is corporate fraud?
Alexander Dyck, Adair Morse, Luigi Zingales
2025· article· en· Review of Accounting Studies· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Racial Minorities and Crime
Narayanan Ganapathy
2023· other· en· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Prisons of the world
2021· other· en· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Decriminalization of drug use in India
Atul Ambekar
2022· article· en· Indian Journal of Social Psychiatry· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Six Kleptocratic Continua
Aditi Gowri
2005· article· en· Journal of Business Ethics· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Introduction
Mohammad A. Tayebi, Uwe Glässer
2016· book-chapter· en· Lecture notes in social networks· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
The Economics of Scams
Derek Pyne, Stan Miles
2017· article· en· SSRN Electronic Journal· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Late modernity and the rise of private security
Rita Abrahamsen, Michael C. Williams
2010· book-chapter· en· Cambridge University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Organized Crime Group Detection
Mohammad A. Tayebi, Uwe Glässer
2016· book-chapter· en· Lecture notes in social networks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Lorsque le crime rapporte
Marie-Michelle Pariseau, Eddy Supeno
2019· article· fr· Criminologie· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Ethical Implications in Money Laundering Detection
R. McKay White, Alexandra Lyn
2024· book-chapter· en· Advances in business strategy and competitive advantage book series· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
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