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Record W7119479348 · doi:10.70062/djls.v1i4.116

Formulation of Criminal Law System Policy in the Settlement of Compensation for Corruption Crimes

2025· article· W7119479348 on OpenAlexaff
Erfan Efendi Yudi Arianto, Suprapto Suprapto, Achmad Faishal, Kamran Azizli

Bibliographic record

VenueDiscourse Journal on Law and Society · 2025
Typearticle
Language
FieldSocial Sciences
TopicIndonesian Legal and Regulatory Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsImprisonmentStatutory lawIncentiveCriminal lawLanguage changeCompensation (psychology)Settlement (finance)State (computer science)

Abstract

fetched live from OpenAlex

Corruption in Indonesia is not merely a moral transgression but an extraordinary economic crime that depletes national resources. The ultimate objective of corruption eradication, therefore, must be the restoration of state financial losses (asset recovery). However, the current formulation of the criminal law system specifically Article 18 of Law No. 31 of 1999 contains a critical policy flaw. The provision of "Subsidiary Imprisonment" (Pidana Pengganti) allows convicts to substitute their financial restitution obligations with a disproportionately short prison term. This mechanism inadvertently provides an economic incentive for corruptors to conceal assets and choose imprisonment, resulting in significant state revenue loss. This study aims to critique the current penal policy formulation and propose a comprehensive reformulation of the compensation system. The research employs a normative-juridical method with a statutory and conceptual approach, utilizing the "Economic Analysis of Law" theory to evaluate the efficiency of sanctions. The study argues that the penal policy must shift from a "person-based" approach (in personam) to an "asset-based" approach (in rem). It is imperative to abolish the subsidiary imprisonment option for high-value corruption and implement "Non-Conviction Based Forfeiture" to maximize the recovery of state losses. Furthermore, this policy shift requires law enforcers with high-level cognitive skills to trace complex financial trails.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.350
Teacher spread0.325 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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