Challenges in Indonesian Environmental Law Enforcement: Handling Individual Culpa Mistake (Negligence) Cases
Bibliographic record
Abstract
This article examines the shortcomings of environmental law enforcement in Indonesia concerning culpa (negligence) by individual offenders. Criminal sanctions in such cases often produce superficial “greenwashing” verdicts, where penalties appear strict yet fail to deliver substantive justice or environmental restoration. Although based on ecological damage assessments, these calculations rarely serve a practical function, as proving negligence becomes secondary and compensation remains unused. As a result, rulings reveal a disconnect between environmental harm and sanctions imposed, with imprisonment and fines disproportionately burdening negligent individuals while offering little ecological benefit. Using a normative juridical approach combining statutory, conceptual, and case analyses, this study finds that the system fosters inefficiency, with costly assessments underutilized and appeals largely abandoned, leaving clemency as the only viable remedy. It argues that community service sanctions focused on ecological rehabilitation would provide a fairer, more feasible, and future-oriented alternative that aligns accountability with environmental recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| 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".