‘They won't change it back in their heads that we're trash’: the intersection of sex work‐related stigma and evolving policing strategies
Bibliographic record
Abstract
In Vancouver, Canada, there has been a continuous shift in the policing of sex work away from arresting sex workers, which led to the implementation of a policing strategy that explicitly prioritised the safety of sex workers and continued to target sex workers' clients. We conducted semi-structured interviews with 26 cisgender and five transgender women street-based sex workers about their working conditions. Data were analysed thematically and by drawing on concepts of structural stigma and vulnerability. Our results indicated that despite police rhetoric of prioritising the safety of sex workers, participants were denied their citizenship rights for police protection by virtue of their 'risky' occupation and were thus responsiblised for sex work related violence. Our findings further suggest that sex workers' interactions with neighbourhood residents were predominantly shaped by a discourse of sex workers as a 'risky' presence in the urban landscape and police took swift action in removing sex workers in the case of complaints. This study highlights that intersecting regimes of stigmatisation and criminalisation continued to undermine sex workers citizenship rights to police protection and legal recourse and perpetuated labour conditions that render sex workers at increased risk for violence and poor health.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.043 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".