A Comparative Study of Restorative Justice in Criminal Law Enforcement in Common Law and Civil Law Countries and Its Implications for Indonesian Legal Reform
Bibliographic record
Abstract
This study aims to analyze and compare the application of restorative justice in the criminal legal systems of Common Law and Civil Law countries, and examine its implications for criminal law reform in Indonesia. The restorative justice paradigm emerged as an alternative to the retributive justice system, which has focused on punishing the perpetrator rather than restoring the victim and the community. Using a normative-comparative juridical approach, this study examines various restorative justice practices in the United Kingdom, Canada, and New Zealand, representing Common Law, and the Netherlands, France, and Indonesia, representing Civil Law. The analysis shows that the Common Law system is more adaptive and provides broad discretion to law enforcement officials to implement restorative-based solutions, while the Civil Law system tends to be more bound by a legalistic framework and requires firm legal regulations before implementation. Nevertheless, both systems show the same direction towards a more humanistic and participatory law enforcement. In the Indonesian context, the application of restorative justice needs to be strengthened through the harmonization of laws and regulations, increasing the capacity of law enforcement officials, and establishing an integrated penal mediation institution. These findings confirm that national criminal law reform should adopt a hybrid approach that combines the flexibility of Common Law with the legal certainty of Civil Law to create a criminal justice system that is socially just and restorative.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".