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Record W3122990922 · doi:10.5539/ass.v6n8p109

The Politics of the Anti-Money Laundering Act of the Philippines: An Assessment of the Republic Act 9160 and 9194

2010· article· en· W3122990922 on OpenAlexvenueno aff
Bing Baltazar C. Brillo

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsLawmakingSanctionsPoliticsMoney launderingGovernment (linguistics)Action (physics)Task forcePolitical scienceLaw and economicsBusinessPublic administrationEconomicsLawLegislature

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.007
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.331
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2010
Admission routes1
Has abstractyes

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