Money laundering : hide and seek : an exploration of international cooperation between law enforcement agencies
Bibliographic record
Abstract
Money laundering is one of the most complex policy challenges facing the Canadian government and various federal and provincial criminal justice agencies.Since the advent of global financial systems based increasingly on sophisticated technology, money laundering has involved multi-national transactions.In response to this phenomenon, Canada has participated in designing international treaties to better facilitate the investigation and prosecution of money laundering schemes.This thesis explores the mutual legal assistance process and how it works at the level of implementation in a Canadian context.The primary policy question is: "Does the legislation and regulations Canada and other countries have enacted to investigate and prosecute money laundering offences reflect those countries' actual capacity and will to provide mutual legal assistance to law enforcement officers in foreign jurisdictions?"This thesis suggests that the answer to this question is both yes and no.A case study approach involving the Royal Canadian Mounted Police's Vancouver Commercial Crime and Proceeds of Crime Sections was employed.Both agencies are involved in mutual legal assistance requests from Canada to foreign jurisdictions as well as from foreign jurisdictions to Canada.Police case files were used to describe the degree to which governments commit to the mutual legal assistance process.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".