Beyond the criminal law: what local and provincial authorities can do to regulate sexually-oriented business
Bibliographic record
Abstract
The proposed new federal prostitution law has attracted a great deal of attention, but few Canadians know much about the alternatives: for example, few know that Australia and New Zealand, though they have very similar criminal codes, have almost completely decriminalized prostitution. Also, it is not widely known that some Canadian cities have long regulated (through licensing) some sex work, in the form of escort services. This talk reviews existing laws and policies, focusing mainly on the common-law jurisdictions of Australia and New Zealand, and also goes over existing Canadian municipal business licensing schemes. It will be shown that some regulatory schemes have proven to be as problematic, for the workers and for authorities, as criminalization. But Canada — and especially its provinces, territories, and municipalities — is in an excellent position to benefit from what has been learned from the over ten years of regulatory experiences in other countries.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".