The Idea of Implementing a Deferred Prosecution Agreement with the Anti-Bribery Management System in Corruption Crime Management by Corporations in Indonesia
Bibliographic record
Abstract
Corporations are entities that have a large role in society, there are many positive roles to life, but not a few negative existences of activities that arise, including corruption. The purpose of this article is to analyze the implementation of the Deferred Prosecution Agreement (DPA) with the Anti-Bribery Management System (ABMS) in dealing with Corruption by Corporations in Indonesia. The method used in this article is normative juridical legal research. This article concludes ways to eradicate corrupt acts carried out remarkably, in turn experiencing obstacles in terms of the functioning of criminal law, even it can be said to be counter-productive. This is a concrete step towards the idea of implementing DPA as a restorative approach in the context of tackling corruption acts committed by corporations. By using the Anti-Bribery Management System (ABMS), it is expected that corrective steps will be obtained from the corporation.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".