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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 ·
Results by year
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.
940 works in the cohort · of 4,299,418page 5 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.

affunlabeled
Factual Difference-Making
Holger Andreas, Mario Günther
2025· article· en· Australasian Philosophical Review· Social Sciences
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Variants of Cops and Robbers
Anthony Bonato, Richard J. Nowakowski
2011· book-chapter· en· Student mathematical library· Social Sciences
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Financial Crimes: Psychological, Technological, and Ethical Issues
Michel Dion, David N. Weisstub, Jean‐Loup Richet
2016· book· en· International library of ethics, law, and the new medicine/˜The œinternational library of ethics, law, and the new medicine· Social Sciences
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Understanding the Delusion of Theft
Mary V. Seeman
2018· review· en· Psychiatric Quarterly· Social Sciences
machine prediction:candidate · noneconsensus · none
7
citations
affaboutunlabeled
Spatial mobility and organised crime
Cameron N. McIntosh, Austin Lawrence
2011· article· en· Global Crime· Social Sciences
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Money laundering consequences
Michelle Gallant
2014· article· en· Journal of Money Laundering Control· Social Sciences
machine prediction:candidate · noneconsensus · none
7
citations
affaboutunlabeled
Money Laundering in Real Estate (RE)
Mark Lokanan, Gaurav Chopra
2021· book-chapter· en· Advances in finance, accounting, and economics book series· Social Sciences
machine prediction:candidate · noneconsensus · none
6
citations
venueno affunlabeled
Les zones urbaines criminelles
Maurice Cusson
2005· article· en· Criminologie· Social Sciences
machine prediction:candidate · noneconsensus · none
6
citations
affaboutunlabeled
Organized crime in business
John Sliter
2006· article· en· Journal of Financial Crime· Social Sciences
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Is money laundering an ethical issue?
Michel Dion
2015· article· en· Journal of Money Laundering Control· Social Sciences
machine prediction:candidate · noneconsensus · none
6
citations
venueno affunlabeled
The Counterfeit Child
Steven Bruhm
2012· article· en· English studies in Canada· Social Sciences
machine prediction:candidate · noneconsensus · none
6
citations
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How this was built: Screen · Findings · About