Law Enforcement against Criminal Acts in Politics in Indonesia Connected with Positive Law
Bibliographic record
Abstract
Political crimes are deemed to be problem, especially regarding their enforcement. Positive law has been set but the political crimes continue to occur. It is presumably caused by unpreparedness of the supporting factors to compensate for sophisticated and varied political crimes, criminal sanctions and a weak political will. As a result, there is a gap because of the breach of the law principles itself. Accordingly, it is necessary to study whether the positive law enforcement can reach all kinds of political crimes, how the criminal policies are formulated and the constraints and solutions to be pursued. In exposing the above issues, this research is descriptive analysis using normative juridical method. Their validity are checked through triangulation examination technique and then analyzed by qualitative analysis. The results revealed that political crimes are crimes against public interest and the occurrence process relates with the power and political activity as their means. If the power and political activity are synergized and strong, the political crimes will find their perfection. Positive law is essentially the result of a series of political processes. Consequently, any enforcement effort of positive law on political crime cannot be completed because political crime always coincides with high-tech, high management and high politic beyond the boundaries of reality (law, morality, culture and common sense). It then develops into a discourse that is planned, organized and controlled to be untouched and unreached crime. Meanwhile, positive law works in a linear-mechanistic way based on doctrine of Legal Positivism or Rechtsdogmatiek by promoting criminal policy in the form of penal policy that in reality had lost much of its authority.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".