The Spatial-Temporal Pattern of Policing Following a Drug Policy Reform: Triangulating Self-Reported Arrests With Official Crime Statistics
Bibliographic record
Abstract
BACKGROUND: In 2009, Mexico enacted a drug policy reform (Narcomenudeo) designed to divert persons possessing small amounts of illicit drugs to treatment rather than incarceration. To assess reform impact, this study examines the spatial-temporal trends of drug-related policing in Tijuana, Mexico post-enactment. METHOD: Location of self-reported arrests (N = 1,160) among a prospective, community-recruited cohort of people who inject drugs (PWID) in Tijuana (N = 552) was mapped across city neighborhoods. Official police reports detailing drug-related arrests was triangulated with PWID self-reported arrests. Exploratory spatial data analysis examined the distribution of arrests and spatial association between both datasets across three successive years, 2011-2013. RESULTS: In 2011, over half of PWID reported being detained but not officially charged with a criminal offense; in 2013, 90% of arrests led to criminal charges. Official drug-related arrests increased by 67.8% (p <.01) from 2011 to 2013 despite overall arrest rates remaining stable throughout Tijuana. For each successive year, we identified a high degree of spatial association between the location of self-reported and official arrests (p <.05). CONCLUSION: Two independent data sources suggest that intensity of drug law enforcement had risen in Tijuana despite the promulgation of a public health-oriented drug policy reform. The highest concentrations of arrests were in areas traditionally characterized by higher rates of drug crime. High correlation between self-reported and official arrest data underscores opportunities for future research on the role of policing as a structural determinant of public health.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".