Bibliographic record
Abstract
In what follows, we build on the lengthy history of sex workers' struggle for human and labour rights in Canada and around the world.In particular, we would like to acknowledge the contributions of Terri-Jean Bedford, Amy Lebovitch, and Valerie Scott.Th ese courageous activists spent years in the courts challenging unjust and harmful laws in an eff ort to improve sex workers' lives.Th at the government introduced the repressive Protection of Communities and Exploited Persons Act in response to their historic Supreme Court of Canada win of 2013 does not diminish their contribution.Also important are the hundreds of sex worker activists and allies who work tirelessly for sex worker rights and agitate for systemic improvements.As editors, we are proud to consider ourselves members of a dynamic, creative, and courageous community fi ghting for social and economic justice.We are similarly proud to be part of a vibrant network of scholars conducting research that challenges dominant discourses and examines the laws, policies, and practices that condition the personal and professional lives of sex workers.We follow in the footsteps of John Lowman and Frances Shaver, researchers we deeply respect and admire, who laid the groundwork for critical sex work studies in Canada.In this book, we endeavour to pay homage to the important contributions of scholars and sex work activists, as well as the many scholar-activists who straddle both roles.Chapter authors are themselves members of diverse activist and scholarly communities, and
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.267 | 0.169 |
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".