Restorative Justice Sebagai Pembaharuan Sistem Peradilan Pidana Berdasarkan UU No.11 Tahun 2012 Tentang Sistem Peradilan Pidana Anak
Bibliographic record
Abstract
ABSTRACT Formation basis of Restorative Justice’s concept in Indonesia originated from a variety of tribal customary law. As customary of Minangkabau, Mandailing, Toraja, Bali and others have done this concept known as the consensus agreement. Restorative Justice’s concept is also known in 1970 in Canada. Restorative Justice’s concept implemented in Article 7 of Law Number 11 of 2012 by using the Diversion way. Potencies are available when applying Restorative Justice’s concept is the Institutional and Professionals of Law Enforcement Officer substantively has been guaranteed in the Law Number 11 of 2012 added many people who support the implementation of Restorative Justice’s concept in Juvenile Justice System in the foreseeable future. Constraints that will be faced by law enforcement officer is the human resources who have not been able to fulfill what is mandated by law Number 11 of 2012, namely the existence of a Judge, Prosecutor and police for child which is a must in every agencies. Inadequate infrastructure in every law enforcement officer agencies is also an obstacle for the future implementation of the Restorative Justice’s concept, and the legal culture of society that many complained by law enforcement officer that can be complicate the implementation of Restorative Justice’s concept in the Juvenile Justice System. Keywords :RestorativeJustice,Juvenile Justice System, Law Enforcement Officer
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.039 | 0.005 |
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".