Rebuilding the Implementation of Sanctions for Ethical Code Violations by Civil Servants by Investigators Based on Values of Justice
Bibliographic record
Abstract
The enforcement of ethical codes within the civil service is a fundamental pillar for maintaining public trust and bureaucratic integrity. However, the implementation of disciplinary sanctions for Civil Servants (Aparatur Sipil Negara or ASN) in Indonesia currently faces significant challenges regarding fairness and consistency. (Problem) The core issue lies in the broad administrative discretion possessed by investigators (Tim Pemeriksa) under Government Regulation No. 94 of 2021, which often leads to subjective, legalistic, and disproportionate sanctioning without considering substantive justice. This study aims to analyze the weaknesses of the current sanction implementation mechanism and proposes a reconstruction of the investigators' authority based on the value of justice (Nilai Keadilan). Using a normative juridical approach and conceptual analysis, this research examines current regulations and compares them with the principles of Dignified Justice. The study finds that the current positivistic approach tends to ignore the human aspect and restorative potential of the sanctions. Consequently, a reconstructed model is proposed where investigators must integrate ethical deliberation and justice values into their examination process, ensuring sanctions are not merely punitive but also corrective and fair.
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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 | 0.001 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".