ANALISIS TINDAK PIDANA NARKOTIKA BERDASARKAN PUTUSAN PENGADILAN NEGERI MAKASSAR REGISTER PERKARA NO.816/PID.SUS/2023/PN. Mks.
Bibliographic record
Abstract
In the context of legal proceedings, courts evaluate factors that may mitigate the sentences given to defendants, such as cooperation with law enforcement, admission of guilt, and repentance. The aim is to balance justice with efforts to reform defendants' behavior.This research analyzes drug offenses based on the verdict of the Makassar District Court in case number 816/Pid.Sus/2023/PN. Mks. The study employs a normative juridical approach to assess the application of law in cases of drug abuse, focusing particularly on the court's decision.The findings reveal that the Makassar District Court's verdict in this case was deemed ineffective as the imposed punishment did not deter the perpetrator, despite clear evidence that the defendant, Risman Jalali Alias Chris, committed a criminal offense involving Class I narcotics for purposes other than personal use, as alleged. Therefore, the defendant received a sentence of 1 year and 6 months in prison, with the time spent in pretrial detention deductedThis evaluation highlights challenges in law enforcement, rehabilitation, and efforts to prevent drug abuse. Prevention and educational initiatives are crucial to reducing drug-related crimes. Therefore, comprehensive approaches are necessary, encompassing effective law enforcement, holistic rehabilitation programs, and sustained efforts in prevention and education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".