Legal Changes and the Decline of Sex Work Arrests in Toronto Neighborhoods, 1992–2020
Bibliographic record
Abstract
In recent decades, sex work in Canada has faced legal changes with efforts to enhance the criminalization of buyers and court challenges to uphold sex workers’ human rights. What are the consequences of these legal changes and challenges on the streets of Toronto where sex work and its enforcement have also changed? Leveraging an intersectional framework with annual data on Toronto police–recorded sex work occurrences and census data from 1992 to 2020 (29 years, 579 tracts, n = 16,791), we spatially and longitudinally analyze neighborhood counts of sex work arrests and their relationship to sex work policies and neighborhood effects. Our results show that (a) the policing of sex work in Toronto has dropped by 99.6% in the past 30 years, (b) neighborhood socioeconomic disadvantage increases neighborhood arrests, (c) criminalizing policies that target buyers increase neighborhood arrests, and (d) the Supreme Court decision upholding sex workers’ human rights briefly decreased neighborhood arrests. Our study raises policy implications as to why laws do not do more to protect sex workers when it appears that arrests have become a low priority for police. Rather than criminalization, laws could prioritize harm reduction, especially for Toronto’s most marginalized sex workers and clients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".