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Record W1993811191 · doi:10.1108/13590790310808970

Canada, crime control and co‐opting legal counsel: canvassing the confidentiality crisis

2003· article· en· W1993811191 on OpenAlexaffabout
Michelle Gallant

Bibliographic record

VenueJournal of Financial Crime · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsConfidentialityObligationDocumentationLegislatureBusinessLawMoney launderingPolitical scienceFalse accusationControl (management)Public interestInvestment (military)Function (biology)CriminologySociologyEconomics

Abstract

fetched live from OpenAlex

Traces the development since 1989 of the Canadian anti‐money laundering framework, which involves the obligation on lawyers to report suspicious transactions and not inform the client about this disclosure; previously, it was not an offence to disguise the criminal origin of assets, but the Canadian regime is now one of the world’s broadest. Reports the resistance of the Canadian legal community to involvement in the campaign, partly because the regime is so broad, confusing and cumbersome; it requires significant investment in documentation and is hazy about the distinction between knowledge and suspicion. Critiques the legislative framework, the solicitor ‐ client relationship and the tension between this confidentiality and the reporting function, including the exceptions to the general rule of confidentiality if it is in the public interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.150
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0350.019
Scholarly communication0.0130.004
Open science0.0020.005
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0080.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.277
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2003
Admission routes2
Has abstractyes

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