Bridging Restorative Justice and Fair Trial: Reconstruction of Closed Trials in the Indonesian Criminal Justice System
Bibliographic record
Abstract
This study aims to formulate a selective closed trial model that allows for the implementation of restorative justice without violating the principle of openness. Restorative justice has become an alternative approach in the Indonesian criminal justice system that places victims at the center of recovery. However, the implementation of restorative justice faces challenges when confronted with the principle of openness in trials, a fundamental principle in national criminal procedure law. The method used in this study is normative juridical with statutory, conceptual, and comparative approaches. By examining provisions in the Criminal Procedure Code (KUHAP), the Supreme Court Regulation on Restorative Justice (PERMA RJ), and practices in other countries such as the United States and Canada, it is found that there is an urgency to establish a legal mechanism that gives judges limited authority to conditionally determine closed trials based on the consent of the parties and the principle of confidentiality. This reconstruction aims to realize a criminal justice system that is fairer, more participatory, and based on the humanitarian values of Pancasila and the mandate of the constitution.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| 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".