Navigating Risk and Safety: An ethnographic analysis of body rub centres in Edmonton, Alberta
Bibliographic record
Abstract
This Master’s thesis focuses on the experiences of indoor sex workers working in body rub centres in Edmonton, Alberta. In Canada, the research on sex work tends to focus primarily on the experiences of outdoor sex workers although the majority of Canadian sex workers work indoors (Hanger, 2006). To address this gap in the literature, I ask two questions: 1. How do indoor sex workers view working in body rub centres in comparison to other sex work settings? 2. How do body rub centres mitigate some of the physical and psychological risks associated with sex work? Using an ethnographic approach to conduct 14 semi-structured interviews, 20 informal interviews, and over 700 hours of fieldwork, my findings outline the importance of body rub centres as sites of risk mitigation for sex workers. I find that body rub centres are associated with increased feelings of safety and a decrease in risks for sex workers in comparison to street-level sex work and indoor sex work in other settings (such as escorting). I also discuss how the unique social organization of body rub centres can mitigate some of the physical and psychological risks sex workers may experience. Finally, I discuss the benefits of the City of Edmonton’s harm-reduction approach to body rub centres and argue that Edmonton’s model should be considered by other Canadian municipalities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| 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".