Sentencing of Minor Offences in Indonesia: Policy, Practice and Reform
Bibliographic record
Abstract
This paper seeks to evaluate minor offenses in the Criminal Code in Indonesia. So far, the value limit for determining minor offenses in Indonesia is increasingly irrelevant to the value of the currency due to inflation. It will cause a gap in criminal law in dealing with changes. As the result, police will do unfair and non-humanistic law implementation. The objective of this study is to identify the importance of revising the lower limit of minor offenses in the Criminal Code in Indonesia. The study used a socio-legal method on the contextualization of Indonesian Criminal Codes related to the categorization of minor offense regulation in Government Regulation No. 2 of 2012 and in Penal Code, by utilizing a humanitarian perspective in law enforcement, especially by police who still charge some minor offenses with 5 years imprisonment. The results confirmed that some changes have been made related to this matter as the Indonesian Supreme Court has made some regulations, such as No. 2 of 2012 on adjustment in minor offense law. This means that all criminals doing minor offenses cannot behold as prisoners in the investigation or prosecution process. The main contribution of this study is to construct a perspective of legal and regulatory issues to emphasize a fair of justice in dealing with minor offenses with a model of humanistic law enforcement. The result is expected to practically contribute and recommend the importance of constructing fairness of justice principle in law enforcement in particular and of revising minor offense sentencing in general.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".