Decolonizing Sex Work in Canada: Assessing the Impact of Government Regulation on the Wellbeing of Indigenous Sex Workers
Bibliographic record
Abstract
Canadian sex work regulation is a historically rooted colonial tool used to control Indigenous bodies and their mobility in Canada. Indigenous women make up 2.5-3% of the general population in Canada, yet they comprise over 50% of street sex workers and data suggests they are also significantly over-represented in other forms of sex work. The close relationship between Canadian colonialism and sex work regulation, and the over representation of Indigenous women in this area suggests that creating sex work legislation that empowers Indigenous sex workers should be considered as a form of reconciliation. To generate lessons for how this form of reconciliation may be achieved in Canada, this paper compares the Nordic and New Zealand Models of government sex work policy by assessing their impacts on Indigenous women sex workers' wellbeing. The Nordic Model, which was adopted in Canada in 2014, was found to have resulted in the reinforcement of negative stereotypes, increased surveillance and policing of Indigenous communities, and the creation of situations which reduce Indigenous sex worker’s health in Canada. By contrast, the New Zealand Model increased Indigenous sex workers wellbeing through decriminalization and espousing a specifically pro-sex worker stance in their policy creation and implementations processes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".