“People like us”: spatialised notions of health, stigma, power and subjectivity among women in street sex work
Bibliographic record
Abstract
Most spatially-oriented studies about health, safety and service provision among women in street sex work have taken place in large urban cities and document how the socio-legal and moral surveillance of geographical spaces constrain their daily movements and compromise their ability to care for themselves. Designed to contribute new knowledge about the broader socio-cultural and environmental landscape of sex work in smaller urban centres, we conducted qualitative interviews and social mapping activities with thirty-three women working in a medium-sized Canadian city. Our findings demonstrate a socio-spatial convergence regarding service provision, violence, and stigma, which is common in sex trading spaces that double as service landscapes for poor populations. Women in this study employ unique agential strategies to navigate these competing forces, many of which draw upon the multivalent uses of different urban spaces to optimise service access, reduce the propensity for violence, and manage their health with dignity. Their use of the spatialised term 'everywhere' as an idiom of distress regarding issues of power, agency and their desire to take part in wider civic discourse are also explored. These data contribute new insights about spatialised notions of health, stigma, agency, and subjectivity among women in sex work and how they manage 'risky' environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.057 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".