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Record W2046047370 · doi:10.1108/13685200310809662

Using confiscated money

2003· article· en· W2046047370 on OpenAlexaboutno aff
Daniel Murphy

Bibliographic record

VenueJournal of Money Laundering Control · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMoney launderingRestitutionTask forceConventionTerrorismCompensation (psychology)Subject (documents)BusinessFinanceLawEconomicsPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

Discusses the sharing of assets confiscated from money launderers by the nations that cooperate against the laundering practice, with special reference to Canada’s approach. Examines what the Financial Action Task Force and international conventions say on this subject. Reviews the fairly recent history of victim compensation in Canada and elsewhere, including Canada’s provisions for forfeiture in terrorism cases and for restitution using a victim fund surcharge on offenders. Suggests that an international convention should clearly direct how forfeited assets should be distributed to the victims of the crime; at present it is difficult for a country that prosecutes to ensure equitable distribution to all victims, especially if they are foreign nationals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0050.010
Scholarly communication0.0070.006
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.051
GPT teacher head0.315
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2003
Admission routes1
Has abstractyes

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