“Pick up anything that moves”: a qualitative analysis of a police crackdown against people who use drugs in Tijuana, Mexico
Bibliographic record
Abstract
BACKGROUND: Homeless people who use drugs (PWUD) are often displaced, detained, and/or forced into drug treatment during police crackdowns. Such operations follow a zero-tolerance approach to law enforcement and have a deleterious impact on the health of PWUD. In Mexico, municipal police officers (MPOs) conducted the largest crackdown documented at the Tijuana River Canal (Tijuana Mejora) to dismantle an open drug market. We analyzed active-duty MPOs' attitudes on the rationale, implementation, and outcomes of the crackdown. We also included the involvement of non-governmental allies in the disguised imprisonment as drug treatment referral and potential legal consequences of having illegally detained PWUD. METHODS: Between February-June 2016, 20 semi-structured interviews were conducted with MPOs in Tijuana. Interviews were transcribed, translated and coded using a consensus-based approach. Emergent themes, trends and frameworks were analyzed through a hermeneutic grounded theory protocol. RESULTS: Participants recognized the limitations of Tijuana Mejora in effectively controlling crime and addressing drug treatment solutions. MPOs perceived that the intent of the operation was to displace and detain homeless PWUD, not to assist or rehabilitate them. The police operation was largely justified as a public safety measure to reduce the risk of injury due to flooding, decrease drug consumption among PWUD and protect local tourism from PWUD. Some participants perceived the crackdown as a successful public health and safety measure while others highlighted occupational risks to MPOs and potential human rights violations of PWUD. CONCLUSIONS: Tijuana Mejora illustrated why public and private actors align in enforcing zero-tolerance drug policy. Perceptions of care are often based on captivity of the diseased, not in health and well-being of PWUD. Officer perceptions shed light on the many limitations of this punitive policing tool in this context. A shift towards evidence-based municipal strategies to address drug use, wherein police are perceived as partners in harm reduction rather than antagonists, is warranted.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".