The Urgency of Enacting Government Regulation on Community Service Sentence in Indonesian under the New Penal Code
Bibliographic record
Abstract
This research aims to contribute by providing a conceptual framework for drafting government regulations on community service sentences from an Indonesian criminal law perspective while also drawing comparisons with practices in Canada and the Netherlands. A normative legal research method is employed, involving a qualitative analysis of the collected legal materials. The findings indicate that community service sentences in Law Number 1 of 2023 signify a paradigm shift in the Indonesian criminal justice system towards a more humane and restorative approach. Comparisons with Canada and the Netherlands offer valuable insights into the effective and just imposition of community service sentences. The principles of restorative justice and utilitarianism, along with the comprehensive regulations in the Canadian Criminal Code and the Wetboek van Strafrecht, can serve as references in formulating robust government regulations in Indonesia. The urgency of establishing these government regulations is underpinned by the need for legal certainty, human rights protection, and optimization of the benefits of community service sentences for convicted individuals, ensuring that this sentence is not merely an alternative punishment but also an effective instrument in achieving the rehabilitation and social reintegration of offenders. Therefore, it is recommended that the Government promptly formulate Government Regulations on community service sentences, considering best practices from other countries and paying attention to relevant laws. It is also recommended that the House of Representatives consider amending Law Number 8 of 1981 regarding the regulation of community service sentence implementation to ensure harmony and legal clarity between the Criminal Procedure Code and the New Penal Code and to provide a solid legal basis for the Public Prosecution Service in carrying out its supervisory duties.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".