Investigative Auditing in Environmental Pollution Cases: An Analysis of Indonesian Supreme Court Decision
Bibliographic record
Abstract
This study investigates the implementation of the investigative audit function within criminal decisions related to environmental pollution cases. The research employs a normative juridical approach, wherein court decisions constitute the primary legal sources and hold a significant position in the legal hierarchy. Specifically, the central legal material scrutinized in this research pertains to the final ruling of the Supreme Court, documented as Decision Number 6978 K/Pid.Sus.LH/2022, dated December 30, 2022. Supporting references encompass pertinent literature, including books and scholarly journals. The findings of this study underscore the indispensability of investigative audits in the context of environmental pollution cases in Indonesia, particularly in adjudicating criminal offenses associated with such pollution. The utilization of the investigative audit function within the criminal decisions for environmental pollution cases is evident through the comprehensive analysis of the aforementioned Supreme Court decision. This decision, rendered by the Panel of Judges of the Supreme Court, effectively demonstrates the application of the investigative audit function. Notably, expert testimony plays a pivotal role in this process. Dr. Ir. Basuki Wasis, M.Sc., conducts rigorous assessments on PT Belfat Indah Permai's land by extracting and analyzing multiple soil samples. The subsequent expert conclusions presented in court serve as a testament to the adept utilization of the investigative audit function throughout the legal proceedings.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".