An ethnographic exploration of factors that drive policing of street-based female sex workers in a U.S. setting - identifying opportunities for intervention.
Bibliographic record
Abstract
Abstract Background: Building on a broader sociological discourse around policing approaches towards vulnerable populations, increasing public health and human rights evidence points to policing practices as a key health determinant, particularly among street-based sex workers. Despite the importance of policing as a structural health determinant, few studies have sought to understand the factors that underlie and shape harmful policing practices towards sex workers. This study therefore aimed to explore the drivers for policing attitudes and practices towards street-based cisgender female sex workers.Methods: Drawing on ethnographic methods, 280 hours of observations with police patrol and 10 stakeholder interviews with senior police leadership in Baltimore City, USA were carried out to better understand the drivers for policing strategies towards cisgender female sex workers. Analysis was data- and theory-driven, drawing on the concepts of police culture and complementary criminological and sociological literature that aided exploration of the influence of the ecological and structural environment on policing practices.Results: Ecological factors at the structural (e.g., criminalization), organizational (e.g., violent crime control), community and individual level (e.g., stigmatizing attitudes) emerged as key to shaping individual police practices and attitudes towards cisgender female sex workers in this setting. Findings indicate senior police support for increased alignment with public health and human rights goals. However, the study highlights that interventions need to move beyond individual officer training and address the broader structural and organizational setting in which harmful police practices towards sex work operate. Conclusions: A more in-depth understanding of the circumstances that drive law enforcement approaches to street-based sex work is critical to the collaborative design of interventions with police in different settings. In considering public health-police partnerships to address the rights and health of sex worker populations in the U.S. and elsewhere, this study supports existing calls for decriminalization of sex work, supported by institutional and policy reforms, neighborhood-level dialogues that shift the cultural landscape around sex work within both the police and larger community, and innovative individual-level police trainings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".