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Record W2081389704 · doi:10.1108/13590790910971766

Uncertainties collide: lawyers and money laundering, terrorist finance regulation

2009· article· en· W2081389704 on OpenAlexaffabout
Michelle Gallant

Bibliographic record

VenueJournal of Financial Crime · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMoney launderingScholarshipContext (archaeology)TerrorismCriminal lawOriginalityOrder (exchange)LawValue (mathematics)Political scienceEconomicsLaw and economicsBusinessFinance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to demonstrate the tentative, highly contingent nature of the contemporary press to impose stringent anti-criminal finance regulatory obligations onto Canadian legal counsel. Design/methodology/approach The approach used in this work is to bring together problems associated with different areas of the anti-criminal finance project in order to demonstrate how these problems compound in the context of the fusion of Canadian lawyers and anti-criminal finance regulation. It draws chiefly on Canadian law and Canadian and international scholarship. Findings This paper shows that the tasking of Canadian legal counsel with additional regulatory burdens continues the pattern of developing legal strategies without paying sufficient attention to the actual results that the strategies produce. Practical implications This paper suggests that any continued construction of an anti-criminal finance apparatus should be accompanied by enhanced study of its actual ability to generate results. Originality/value Most investigations of anti-criminal finance developments assume the effectiveness of a strategy focused on detecting and intercepting resources linked to crime. Rather than assume its effectiveness, this paper demonstrates that an extraordinarily level of uncertainty animates that development.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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 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
Published2009
Admission routes2
Has abstractyes

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