Criminalisation of clients : reproducing vulnerabilities for violence and poor health among street-based sex workers in Canada : a qualitative study
Bibliographic record
Abstract
Objectives To explore how criminalisation and policing of sex buyers (clients) rather than sex workers shapes sex workers’ working conditions and sexual transactions including risk of violence and HIV/sexually transmitted infections (STIs). Design Qualitative and ethnographic study triangulated with sex work-related violence prevalence data and publicly available police statistics. Setting Vancouver, Canada, provides a unique opportunity to evaluate the impact of policies that criminalise clients as the local police department adopted a sex work enforcement policy in January 2013 that prioritises sex workers’ safety over arrest, while continuing to target clients. Participants 26 cisgender and 5 transgender women who were street-based sex workers (n=31) participated in semistructured interviews about their working conditions. All had exchanged sex for money in the previous 30 days in Vancouver. Outcome measures Thematic analysis of interview transcripts and ethnographic field notes focused on how police enforcement of clients shaped sex workers’ working conditions and sexual transactions, including risk of violence and HIV/STIs, over an 11-month period postpolicy implementation (January–November 2013). Results Sex workers’ narratives and ethnographic observations indicated that while police sustained a high level of visibility, they eased charging or arresting sex workers and showed increased concern for their safety. However, participants’ accounts and police statistics indicated continued police enforcement of clients. This profoundly impacted the safety strategies sex workers employed. Sex workers continued to mistrust police, had to rush screening clients and were displaced to outlying areas with increased risks of violence, including being forced to engage in unprotected sex. Conclusions These findings suggest that criminalisation and policing strategies that target clients reproduce the harms created by the criminalisation of sex work, in particular, vulnerability to violence and HIV/STIs. The current findings support decriminalisation of sex work to ensure work conditions that support the health and safety of sex workers in Canada and globally.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".