LAYERED HYBRID MODEL IN CRIMINAL RESOLUTION: Integrating Epkeret and State Law Under Legal Pluralism in Indonesia
Bibliographic record
Abstract
Abstract: The ratification of the New Criminal Code (Law No. 1 of 2023) marks a paradigm shift towards restorative justice by recognizing the fulfillment of customary obligations as a valid criminal sanction. However, the absence of procedural technical guidelines creates a significant gap in the integration of customary law into the formal justice system, which could lead to legal uncertainty. This study aims to develop an operational framework using a “Layered Hybrid Model” to bridge customary criminal law and state law without violating human rights. Using sociological-legal methods and empirical data from South Buru Island, Maluku, as well as comparative analysis of customary courts in New Zealand and Canada, this study formulates a two-tiered mechanism. The first tier places customary law (Epkeret) as the primum remedium for social restoration, while the second tier establishes state law as the ultimum remedium for serious crimes. This article offers the first operational institutional design for customary criminal justice in Indonesia by proposing a State-Community Validation Forum as a constitutional filter mechanism. This model encourages a transition from weak legal pluralism to “strong and controlled legal pluralism,” ensuring that customary justice is constitutionally valid and operationally applicable in a modern criminal justice system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".