Justice Sector Corruption: Will Indonesia Neutralize it?
Bibliographic record
Abstract
Background: Though there are vigorous efforts made to fight corruption attitude and behavior, in Indonesia the judiciary sector is still characterized by the existence of rampant widespread corruption acts of crime. For instance, there are many judges who have been caught being bribed across the country. From the available data, of the 19 judges at the Corruption Eradication Commission, 53% are those who make up the Corruption Adhoc judges, while the remaining 47% are career judges. Objective: This research was conducted to determine the corrupt behavior of judges in relation to carrying out their duties and authority in upholding justice. Method: The study applied a normative juridical research method, which established that corruption behavior exhibited by judges in handling cases is still prone to criminal acts of corruption, is detrimental to justice seekers. Conclusion: Thus, the judge's corrupt behavior as the foremost law enforcer can be prevented as early as possible, if justice is to be upheld at a national level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".