The Spatial-Temporal Pattern of Policing Following a Drug Policy Reform: Triangulating Self-Reported Arrests With Official Crime Statistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it