Combating Money Laundering and the \n Financing of Terrorism - A Comprehensive Training Guide : Workbook 4. Building an Effective Financial Intelligence Unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
"Combating Money Laundering and the \n Financing of Terrorism: a Comprehensive Training Guide" \n is one of the products of the capacity enhancement program \n on Anti-Money Laundering and Combating the Funding of \n Terrorism (AML/CFT), which has been co-funded by the \n Governments of Sweden, Japan, Denmark, and Canada. The \n program offers countries the tools, skills, and knowledge to \n build and strengthen their institutional, legal, and \n regulatory frameworks to successfully implement their \n national action plan on these efforts. This workbook \n includes seven training course modules: effects on economic \n development and international standards (module one); legal \n requirements to meet international standards (module two); \n regulatory and institutional requirements for AML/CFT \n (module three a ); compliance requirements for financial \n institutions (module three b); building an effective \n financial intelligence unit (module four); domestic \n (interagency) and international cooperation (module five); \n combating the financing of terrorism(module six); and \n investigating money laundering and terrorist financing \n (module seven).
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it