The Concept Of Restoring The Rights Of Victims Of Mass Fraud With Justice
Bibliographic record
Abstract
Mass fraud is an increasingly prevalent economic crime in Indonesia, involving sophisticated schemes such as Ponzi investments, fictitious savings, and digital scams that have caused systemic financial losses to society. Although criminal proceedings often result in the incarceration of perpetrators, the rights of victims, especially restitution and compensation, remain neglected. This research examined the development of a fair recovery model for victims of mass fraud within Indonesia’s legal system, integrating theories of justice (Rawls), victimology, and restorative justice. The normative-juridical approach employed in this study was supported by a comparative analysis of successful victim compensation frameworks from the Netherlands and Canada, including the Schadefonds Geweldsmisdrijven and the Criminal Injury Compensation Program. Findings indicate that Indonesia’s current legal mechanisms—such as Article 98 KUHAP and Law No. 31/2014 on Witness and Victim Protection—are substantively progressive but practically ineffective due to regulatory gaps, bureaucratic complexity, and lack of victim-oriented enforcement. The study proposes a victim-centric recovery model through: (1) the establishment of a state-managed Victim Compensation Fund; (2) the integration of restitution into prosecutorial practice by enhancing the role of public prosecutors as dominus litis; and (3) harmonizing criminal and civil procedures under a restorative justice paradigm. This conceptual reconstruction aims to shift Indonesia’s penal policy from perpetrator-focused punishment to a balanced system that ensures substantive justice and comprehensive recovery for victims of mass fraud.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".