Combating Money Laundering and the Financing of Terrorism - A Comprehensive Training Guide : Workbook 4. Building an Effective Financial Intelligence Unit
Bibliographic record
Abstract
"Combating Money Laundering and the Financing of Terrorism: a Comprehensive Training Guide" is one of the products of the capacity enhancement program on Anti-Money Laundering and Combating the Funding of Terrorism (AML/CFT), which has been co-funded by the Governments of Sweden, Japan, Denmark, and Canada. The program offers countries the tools, skills, and knowledge to build and strengthen their institutional, legal, and regulatory frameworks to successfully implement their national action plan on these efforts. This workbook includes seven training course modules: effects on economic development and international standards (module one); legal requirements to meet international standards (module two); regulatory and institutional requirements for AML/CFT (module three a ); compliance requirements for financial institutions (module three b); building an effective financial intelligence unit (module four); domestic (interagency) and international cooperation (module five); combating the financing of terrorism(module six); and investigating money laundering and terrorist financing (module seven).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.043 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".