Bibliographic record
Abstract
Around the world, the labour movement has begun to recognize that women employed in the sex industry are deserving of collective bar - gaining rights and protection. However, despite such recognition, exotic (lancers in Canada have remained unable to access such rights. This paper explores the potential benefits of collective bargaining rights to exotic dancers and the methods by which such rights might be attained in Ontario. In doing so, the author argues that collective bargaining rights would be an effective means of not only ameliorating exotic dancers' terms and conditions of work, but also of providing a sense of legitimacy and empowerment to an otherwise stigmatized profession. Though there are a number of barriers to organizing, there are avenues through which collective bargaining rights may be achieved. For example, house dancers, who work exclusively for one club, may claim their status as dependent contractors under the Labour Relations Act and access their rights to organize and bargain. Meanwhile, those dancers employed on a freelance basis for a number of clubs may more appropriately gain access to collective bargaining rights through a statutory scheme similar to that of the Status of the Artist Act, in which collective bargaining rights are afforded to artists employed as independent contractors. It is through such avenues that exotic dancers may finally be clothed and protected with collective bargaining rights.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 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".