Policy and Role of the Criminal Police in Combating Environmental Crimes
Bibliographic record
Abstract
Environmental crimes threaten ecosystems, public health, and global economic stability, necessitating enhanced law enforcement strategies. This study critically examines the role of the criminal police in combating these crimes, focusing on evaluating the effectiveness of police interventions and identifying key factors influencing success. A mixed-methods approach was employed, incorporating a thematic analysis of investigative materials, a quantitative assessment of crime statistics, and qualitative interviews with law enforcement officers and environmental experts. Additionally, spatial analysis using Geographic Information Systems was utilized to identify crime hotspots. The study's findings reveal a clear negative correlation between enhanced policing efforts and environmental crime rates, with spatial analysis highlighting concentrated areas of illegal activity. Qualitative results indicate opportunities for refining enforcement strategies, particularly through technology integration and inter-agency collaboration. The study's contribution lies in its comprehensive approach, combining statistical, spatial, and qualitative data to assess the effectiveness of policing strategies. Future research should focus on long-term evaluations and cross-jurisdictional comparisons to optimize global enforcement practices.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".