Formulation of Correctional System Model in Corruption Enforcement in Indonesia
Bibliographic record
Abstract
Corruption in Indonesia requires the right formulation. The rule of law and punishment system in Indonesia is still weak against criminals. This paper tries to find the right formula in punishing corruptors because the existing punishment system is still breakable and is deemed ineffective in preventing it. This paper belongs to a type of normative approach with a descriptive-analytical. This paper reveals that there are weaknesses in the rule of law in corruption compromise, resulting in increasing corruption every year. Data from ICW in 2018 shows that corruption is still considered a common crime, and the current punishment system does not reflect the firmness in determining the maximum punishment. It is exacerbated by the sale and purchase of facilities in prisons, which results in public distrust of the law. In other countries, Indonesia must be more severe in punishing bribery and corruption, including extraordinary crimes against the people. The conclusion in this paper is to formulate a religious and moral approach in punishing corruptors such as social sanctions, public humiliation, being a cleaning worker, life imprisonment, being exiled, taking political rights, even being humiliated in public so that it becomes a new formula in corrupt penalty 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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".