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Record W2058084179 · doi:10.1108/13685200410810038

International anti‐money laundering and anti‐terrorist financing: the work of the Office of the Superintendent of Financial Institutions in Canada

2004· article· en· W2058084179 on OpenAlexaboutno aff
Nicolas W. R. Burbidge

Bibliographic record

VenueJournal of Money Laundering Control · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMoney launderingFinanceFinancial institutionTerrorismDue diligenceBusinessWork (physics)Task forceFinancial servicesAccountingPublic administrationLawPolitical science

Abstract

fetched live from OpenAlex

Focuses on the work of Canada’s Office of the Superintendent of Financial Institutions (OSFI( in relation to international cooperation against money laundering and terrorism. Outlines Canada’s international obligations to the Financial Action Task Force, International Monetary Fund, United Nations, and to the international bodies concerned with customer due diligence and related matters: these are the Basel Committee on Banking Supervision and the International Association of Insurance Supervisors. Moves on to OSFI’s relationship with the Financial Transactions and Reports Analysis Centre of Canada (FINTRAC(. Lists some of the categories of higher risk that federally regulated financial institutions (FRFI( are expected by OSFI, following international regulators, to recognise as far as customer identification standards are concerned. Summarises OSFI’s anti‐money laundering guidelines. Indicates other areas of financial crime that OSFI combats: financial institution identity theft and advance fee scams, and the growing of marijuana plants in “grow houses”.

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)0.004
metaresearch head score (Gemma)0.008
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.127
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0180.004
Scholarly communication0.0140.003
Open science0.0020.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.245
Teacher spread0.226 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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