Condoms and sexual health education as evidence: impact of criminalization of in-call venues and managers on migrant sex workers access to HIV/STI prevention in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: Despite a large body of evidence globally demonstrating that the criminalization of sex workers increases HIV/STI risks, we know far less about the impact of criminalization and policing of managers and in-call establishments on HIV/STI prevention among sex workers, and even less so among migrant sex workers. METHODS: Analysis draws on ethnographic fieldwork and 46 qualitative interviews with migrant sex workers, managers and business owners of in-call sex work venues in Metro Vancouver, Canada. RESULTS: The criminalization of in-call venues and third parties explicitly limits sex workers' access to HIV/STI prevention, including manager restrictions on condoms and limited onsite access to sexual health information and HIV/STI testing. With limited labour protections and socio-cultural barriers, criminalization and policing undermine the health and human rights of migrant sex workers working in -call venues. CONCLUSIONS: This research supports growing evidence-based calls for decriminalization of sex work, including the removal of criminal sanctions targeting third parties and in-call venues, alongside programs and policies that better protect the working conditions of migrant sex workers as critical to HIV/STI prevention and human rights.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".