Formulation of Criminal Law System Policy in the Settlement of Compensation for Corruption Crimes
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".