Micromanaging the Massage Parlour: How Municipal Bylaws Organize and Shape the Lives of Asian Sex Workers
Bibliographic record
Abstract
Debates on sex work in Canada have focused on its criminal aspects, its harms, and the ‘nuisance’ it posits for communities. Not much attention has been devoted to more subtle forms of regulation that take place in other juridical spaces, such as the legal apparatus of municipalities. A growing number of cities are restricting sex work and using micro-regulation to police, constrain and control the activities and lives sex workers. Municipal bylaws are used by local governments to suppress sex work and prosecute sex workers. By ‘micro-regulation’ I mean the devious introduction of a myriad of rules that strictly constrain the range of activities that can take place in massage parlours, effectively creating barriers to sex work, even though municipalities do not have the power to prohibit the offering of sexual services. This paper aims to alleviate a lacuna in the research and literature on sex work in the juridical space of the municipality. I wish to bring attention specifically to the plight of Asian sex workers employed in massage parlours in the city of Toronto, whose voices and struggles are ignored and go unnoticed. My data was collected during a practice-based research study conducted in Toronto in 2014. My findings will show that municipal bylaws create barriers to the practice of sex work in the context of holistic centres, barriers that push sex work underground, endangering sex workers and exacerbating their stigmatization and exploitation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.031 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".