Victimization in the Canadian Off-Street Sex Industry
Bibliographic record
Abstract
This nation-wide study examined victimization in Canadian off-street commercial sex. Working in collaboration with sex workers, I recruited 109 adult women, men and transgender sex workers to take part in a self-administered survey, and I interviewed 42 sex workers. The survey focused on rates of several forms of violence, including threats, threats with weapons, assault, sexual assault, and confinement. Other forms of victimization included: theft, harassment, the refusal to use condoms, refusal to pay full price for services provided, and pressure to provide sexual activities beyond the worker's service parameters. Participants identified the perpetrators of their victimization—clients, co-workers, bosses, police, significant others—and the frequency with which they experienced victimization. In addition, I collected biographical information and data on risk management, crime reporting practices, and the real and perceived effects of criminal, family, taxation, and immigration laws.My participants described a wide range of experiences in several types of off-street commercial sex work, including adult film, exotic dance, online adult entertainment, and fetish-related erotic labour. A majority of the participants reported never experiencing violence in the course of their sex work (68% or 74 of 109 participants). While victimization occurs in the off-street sex industry, the findings demonstrate that violence is not inherent to commercial sex exchanges. Consequently, to reduce the types and frequency of violence experienced by off-street sex workers, we need to understand the individual, contextual, and structural factors that lead to varying levels of victimization in different sectors of the sex industry. In this dissertation, I outline the existing evidence on victimization in off-street sex work and then I present the evidence gained through this study. I explain the legal implications of the findings and demonstrate how this evidence contrasts with the assumptions that form the basis of criminalization policies in Canada and globally. Finally, I describe sex workers’ recommendations to increase safety and reduce stigma in the sex industry. My participants challenged dominant and oppressive discourses about their work and suggested that the Canadian commercial sex industry is diverse and complex. Our policy responses ought to reflect a nuanced understanding of victimization in commercial sex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".