Money Laundering Impacts: Recovering Wealth, Piercing Secrecy, Disrupting Tax Havens and Distorting International Law
Bibliographic record
Abstract
Money laundering regulation emerged in the later part of the 20th century as a strategy for dealing with the global trade in illegal drugs. It is based on the idea that criminal activity with significant financial underpinnings can better be tempered by the specific targeting of those underpinnings. It is a strategic approach that has grown strikingly over the course of more than thirty years, expanding from drug crimes to tax evasion, to corruption, to organized crime and to terrorism. It has stretched to cover all manner of financial activity from the deposit of monies into bank accounts to visa declarations and casinos operations and from donations to charitable entities to the delivery of professional services. It has spawned a modern regulatory environment forged of international treaties, of domestic law, of guidelines, of recommendations, of principles, of policies, of practices and of commitments.An ambitious project, the impact of this initiative is often the subject of dispute, much of which centers on whether regulation adequately, if at all, moderates criminal activity. Rather than entering into that debate, this paper takes a long view of three decades of development and identifies five particular consequences attributable to money laundering regulation. Ranging from the deprivation of tainted wealth to attending to tax havens and the opacity of international finance, these consequences, not all clearly foreseen, demonstrate tangible impacts of the regulatory effort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".