RESTORATIVE JUSTICE SEBAGAI WUJUD HUKUM PROGRESIF DALAM PERATURAN PERUDANG-UNDANGAN DI INDONESIA
Bibliographic record
Abstract
Currently, there are many settlements of criminal acts carried out using a Restorative Justice approach, both at the police, prosecutor's and court levels. This certainly shows positive things related to law enforcement in Indonesia. Indonesia as a country that adheres to a civil law legal system that prioritizes positive law in its law enforcement process. One of the characteristics of the civil law legal system is the judge as a mouthpiece of the law. The concept of a restorative justice approach is an approach that focuses on the conditions for creating justice and balance, for perpetrators of restitution or compensation for victims, this is one of the goals of law, namely justice apart from legal certainty and benefit. As for the formulation of the problem in this study, how is the concept of restorative justice as a form of progressive law in Indonesian statutory regulations?. The purpose of this study is to determine the concept of restorative justice as a form of progressive law in Indonesian legislation. The results of the study indicate that the application of restorative justice has been applied in several laws and regulations and other technical regulations. The application of restorative justice has been implemented in several laws and regulations and several technical regulations related to the application of restorative justice with a deversion approach, namely the settlement of cases outside the trial. That the application of restorative justice is a new milestone in the reform of criminal law reform (criminal justice system) which still prioritizes prison law.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".