Bibliographic record
Abstract
Purpose To explore how lawyers are used to launder the proceeds of criminal activity. Regulatory measures that compel legal professionals to report suspected money laundering, and the implications this has for solicitor‐client privilege, are also addressed. Design/methodology/approach Data were collected from a sample of Royal Canadian Mounted Police proceeds of crime (POC) case files using a standardized questionnaire. Findings A statistical analysis reveals that lawyers came into contact with the POC in 49.7 percent of all RCMP cases examined. Lawyers are implicated in money laundering (both wittingly and unwittingly) primarily through their role as an intermediary in a commercial or financial transaction. In the majority of these cases, lawyers were facilitating a real property transaction by an individual engaged in drug trafficking. Lawyers were also used by offenders or their nominees to incorporate companies, purchase securities, and conduct bank transactions, including those pertaining to legal trust accounts. Research limitations/implications The analysis of money laundering is based exclusively on an analysis reliance of police cases. The RCMP database from which the sample was drawn was not as complete as originally thought. Practical implications Associations representing the legal profession have vehemently resisted mandatory reporting obligation, arguing that it abrogates solicitor‐client privilege. This paper supports the tacit consensus emerging internationally that mandatory reporting for legal professionals should apply only to the financial and commercial transactions mediated by lawyers on behalf of clients. Originality/value This research helps to inform the debate over the extent to governments should mandate lawyers to report transactions or clients that may be involved in money laundering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.217 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".