The Politics of the Anti-Money Laundering Act of the Philippines: An Assessment of the Republic Act 9160 and 9194
Bibliographic record
Abstract
The article is about the influence of international organizations on the policymaking process. It contends that the procedure followed in enacting Republic Act 9160 otherwise known as the Anti-Money Laundering Act (AMLA) and its amendment Republic Act 9194, as a financial regulation policy is atypical. The enactment of the policy exhibited an unusual pattern in the policymaking process. The AMLA was largely exogenously driven and was enacted mainly by virtue of external pressure. The policy was passed principally to satisfy the demand of the Financial Action Task Force (FATF) to conform to the global standard, to beat the deadline, to avoid the imposition of countermeasures, and to be removed from the list of Non-Cooperative Countries and Territories (NCCT). To avoid sanctions, the Philippine Government made extraordinary efforts to ensure compliance, such as the collaboration shown by the executive agencies, the swift action taken by the legislators, the circumvention of rules and procedures, and the manipulation of the Bicameral Conference Committee (BCC). The steps taken speak of the tremendous influence an international organization can have on the policy actors and the lawmaking proceedings; how an external entity would dictate to institutional actors and regulate the policymaking process.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".