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Record W2101138849

Money laundering : hide and seek : an exploration of international cooperation between law enforcement agencies

2004· dissertation· en· W2101138849 on OpenAlexaboutno aff
Lesley M Bain

Bibliographic record

VenueSummit (Simon Fraser University) · 2004
Typedissertation
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMoney launderingCommitLaw enforcementLegislationContext (archaeology)BusinessGovernment (linguistics)EnforcementPolitical scienceCriminal justiceLawPublic administration
DOInot available

Abstract

fetched live from OpenAlex

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.iii

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.279
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2004
Admission routes1
Has abstractyes

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