The Role of Law Enforcement in Community-Based Drug Treatment and its Impact on Crime Prevention
Bibliographic record
Abstract
In line with trends in other countries the Royal Thai Police acknowledges the need for more community-oriented approaches which are responsive to local contexts. However, the development of such approaches to policing needs also to engage with responses to illicit drug use locally, which would include a wider definition of harm reduction and accommodate the work of public health partners their initiatives, such as needle-exchange. The present study examines the role of law enforcement officers in community-based drug treatment in the Chiang Mai region of the Upper North, through use of in-depth, qualitative interviews with key stakeholders, which included senior police, judges, public health managers, NGO workers and local community leaders. Most interviewees expressed concern that not enough was being done to address drug use in local communities, and barriers to police adopting a harm reduction approach locally included government-directed arrest quotas and lack of experience in working co-operatively with public health partners. Effective police involvement required coordinated policy-change and officer training to develop understanding and ways of working to support community-based drug treatment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".