Bibliographic record
Abstract
This study examines green criminology and its reflection in the Afghan Penal Code (Articles 807 to 850). The main research question is to what extent the principles of green criminology have been incorporated into Afghanistan's criminal policy. The research methodology is based on content analysis of legal provisions and a comparative review with the principles of green criminology. The findings indicate that despite the legislator's efforts to criminalize certain environmental violations, there are shortcomings such as the failure to criminalize some significant environmental offenses, weaknesses in monitoring the activities of foreign companies, and the absence of effective policies for environmental damage compensation. This study suggests that to address environmental crises, legal mechanisms should be strengthened, regulatory institutions should be expanded, and environmental compensation policies should be revised. In recent years, environmental concerns and their consequences have highlighted the necessity of criminalizing environmental offenses more than ever. Green criminology, as an emerging branch of criminology, focuses on studying environmental crimes, environmental criminal policies, and preventive strategies. This research examines Articles 807 to 850 of the Afghan Penal Code from the perspective of green criminology and analyzes their alignment with its principles. The findings show that despite Afghanistan's legislative efforts to criminalize certain environmental violations and crimes, there are still deficiencies such as the lack of criminalization of some critical environmental harms, weak oversight of foreign companies' operations, and the absence of effective policies for environmental damage compensation. This study recommends that to combat environmental crises, legal frameworks should be reinforced, regulatory institutions should be developed, and environmental compensation policies should be reassessed.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".