Punishing Environmental Offenses Without Guilty Mind: Regulatory Framework and Judicial Responses
Bibliographic record
Abstract
This paper explores the legal considerations and the scope of application of criminal liability without guilty mind in environmental offenses in Indonesia. Under the existing Environmental Law, liability without fault has been applicable exclusively in civil cases. This paper combines literature and induction research methods. The first method dives into the legal provision in Environmental Law containing the formulation of the offenses, while the induction method refers to the analysis on judicial decisions in the application of liability without fault. The findings of the study show that most of the prohibited offenses in environmental legislation deal with the malum prohibitum crime tied to the violation of a permit. The mental element is not explicitly stated in these offenses. Hence, the culpability of the defendant is presumed to be displayed in the evidence of the prohibited conduct. Waste or emissions discharged into the environmental media without authorisation is prohibited and pertaining the potential to harm the environment. These offenses are included as formal offenses by removing the element of culpability in the structure of the offense. It is also sufficient for the court to rule that the defendants have committed the prohibited conduct as the basis for imposing criminal sanction.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".