MODEL HARMONISASI HUKUM PIDANA DAN PERDATA UNTUK PENYELESAIAN KASUS KEKERASAN
Bibliographic record
Abstract
Violent crimes often involve two legal aspects, namely criminal and civil, which require harmonization to ensure substantive justice for victims and perpetrators. The main problem faced is the separation of criminal and civil legal processes, which often hinders the holistic restoration of victims' rights. This study aims to analyze the harmonization mechanism of criminal and civil laws applied in various countries, including the United Arab Emirates, Canada, Germany, and Indonesia, and to develop a legal harmonization model that is appropriate to the Indonesian context by considering local values and international standards. The research method used is normative with a statutory regulatory approach, conceptualization, comparison, and history, which focuses on the analysis of relevant legal regulations and legal theories. The results of the study indicate that legal harmonization can be achieved through integrated application, restorative justice mechanisms, and recognition of customary legal practices that do not conflict with human rights. A model that calls for the importance of national legal reform, integration of local values, and application of international standards to create a more responsive legal system. The main recommendations are strengthening legal infrastructure, training law enforcement officers, and implementing best practices from other countries.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".