Bylaws and Brothels: An analysis of Toronto's adult entertainment governance strategy
Bibliographic record
Abstract
Planning for the adult entertainment and sex industry is incredibly controversial. Issues of morality, safety, and health all play major roles on where adult entertainment and sex establishments should be located in cities. Given the municipal government’s role in land-use planning it holds strategic position to influence where legal bawdy-houses (brothels) are located. Municipalities have taken a variety of approaches to regulating the location of adult entertainment establishments. As more jurisdictions legalize brothels, municipalities will need to ensure that these establishments are located in the optimal location to ensure safety and security for the workers, their patrons, and the general public. The report has taken an investigative nature and has identified areas for Toronto to develop and improve policies and governance tools related to regulating the sex industry, more specifically inclusion of brothels as a type of sex establishment. The recommendations of this report are geared to municipal decision makers and aim to begin the discussion and set out a strategic direction on this important and pressing planning topic. It is also important, however, that the political climate is considered prior to implementation of any of the recommendations outlined in this report. It is imperative for the safety of the community, sex workers, and their clients that municipalities begin this policy discussion now. It is clear that, although not at the forefront of municipal policy-makers, this topic is of interest.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".