Nordic or Not: Examining the Future of Canada’s Prostitution Legislation
Bibliographic record
Abstract
I will inquire into the future of prostitution legislation in Canada, given the recent Supreme Court decision to strike down the Criminal Code provisions restricting practices around the sale and purchase of sex. I will consider three models for Canada’s new laws: criminalization, legalization/regulation and the “Nordic model”. I will outline the features of each system, with a particular focus on the Nordic model which many have proposed as the best avenue for Canadian legislators to take. In both Canada and places where a Nordic Model has been implemented, I will account for the legal provisions regarding prostitution, associated laws related to labour, public health, zoning and public nuisance, political factors including the presence and strength of a social safety net and the sort of government that oversees the administration of said social safety net, and the sorts of people who practice prostitution. I will compare the social factors and demographic information from the Nordic states with the Canadian context, establishing similarities and differences between the situations. Ultimately, I will discuss the implications each system in the Canadian context, addressing the likelihood of each model’s implementation, its efficacy at achieving its stated aims and what sorts of conditions it creates for people engaged in prostitution.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".