Money Laundering in Canada: Chasing Dirty and Dangerous Dollars
Bibliographic record
Abstract
Money laundering is the process of converting or transferring cash or other assets, generated from illegal activity, in order to conceal or disguise their origins. In recent years, the international community has decided that focusing on money laundering is an efficient strategy in policing organized crime and, now terrorism. To this end, countries are encouraged to harmonize their policies and legislation and, to some extent, their policing strategies. Before adopting these new strategies, however, it is important to understand the extent of money laundering in different jurisdictions, as well as the likelihood of success and the costs involved in these anti-laundering strategies.\nThis new work by Margaret E. Beare and Stephen Schneider brings empirical evidence to the study of money laundering in Canada – a topic that has recently assumed an international profile. They challenge the seemingly common sense notion, fueled by political posturing and policing rhetoric, that taking the profits away from criminals is a rational law enforcment strategy. Using data from police cases, the inner working of financial institutions, and the 'successful' claims of privilege from our legal profession, the final picture that the authors paint is of a good enforcement strategy run amuch amid conflicting interests and agendas, an overly ambitious set of expectations, and an ambiguous body of evidence as to the strategy's overall merits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".