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Record W223950640

License to Loot? A Critique of Follow-the-Money Methods in Crime Control Policy

2001· article· en· W223950640 on OpenAlexaff
R.T. Naylor

Bibliographic record

VenueSocial Justice A Journal of Crime Conflict & World Order · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsMcGill University
Fundersnot available
KeywordsMoney launderingLaw enforcementLawLicenseCommitEnforcementEconomicsBusinessLaw and economicsFinancePolitical science
DOInot available

Abstract

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Taken to the Cleaners? OVER THE LAST 1.5 YEARS, THERE HAS BEEN A QUIET REVOLUTION IN THE THEORY and practice of law enforcement. Instead of simply closing rackets that generate illegal income, the central objective has become to attack the flow of criminal profits after they have been earned. The justification is that taking away assets accumulated by criminals simultaneously removes the motive (profit) and the means (operating capital) to commit further crimes. Prodded on first by the U.S., then by the Financial Action Task Force of the OECD, and more recently by the United Nations, the pursuit of the of has been elevated to the status of a Crusade. A new crime -- money laundering -- has been put on the books of many countries. Various new reporting requirements have been imposed on financial institutions, which have been forcibly recruited into the struggle. In addition, law enforcement agencies now host special units responsible for arresting not malefactors, but bank accounts, investment portfolios, houses, cars, and even Rolex watches. These initiatives are closely related. Anti-money-laundering regulations require detailed information that helps to create a paper trail to aid tracing and seizing criminal money. They also create new offenses to justify such seizures. (1) To various degrees in various places, the new laws have undermined traditional presumptions in favor of financial privacy, muddled civil and criminal procedures, and opened previously confidential tax records to police probes. Particularly, though not exclusively, in the U.S. these laws have been accused of reversing the burden of proof, smearing citizens with the taint of criminality without benefit of trial, and converting police forces into self-financing bounty-hunting organizations. Some might argue that this is a necessary and justifiable response to an overarching social evil. Some might agree in principle that such a strategy can help to deal with a very serious problem, but criticize its worst abuses. However, some might suggest that the entire exercise is simply insane. Clearly, these new legal initiatives are powerful weapons. It is reasonable to ask that they not be deployed unless and until their need has been unambiguously established, their objectives clearly delineated, and the public well informed of their actual (as distinct from nominal) purposes and of any collateral damage their use might entail. It should be convincingly demonstrated that any perceived failure of existing methods of crime control results from deficiencies of existing laws, rather than from deficiencies in the application of existing laws, that a crisis of sufficient order of magnitude exists to require radical alternatives, and that such alternatives have a good chance of being effective in rectifying those deficiencies. Despite the rapid spread of anti-money-laundering regulations and asset-forfeiture laws across the world, no one really knows how much criminal income and wealth actually exists, how illegal gains are distributed, or how (if at all) deleterious their impact on legitimate society really is. No one can say with any confidence what impact a follow-the-money strategy might have on its intended target, though there is more clarity regarding some of its pernicious side effects. A Solution in Search of a Problem Contemporary laws that facilitate the confiscation of criminally derived assets are of two main types. Some involve in personam procedures, in which an individual must be charged with a crime and that crime proven beyond a reasonable doubt, before specified property, also proven on criminal criteria to be the proceeds of that crime, can be seized. Standard safeguards -- presumption of innocence, right to counsel, and protection against double jeopardy or disproportionate punishment -- apply. Other laws involve in rem procedures, whereby property can be seized if it can be established by civil criteria that the property was the proceeds of or means to commit a crime. …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.425
Teacher spread0.384 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations10
Published2001
Admission routes1
Has abstractyes

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