Beyond Tales of Trafficking: A Needs Assessment of Asian Migrant Sex Workers in Toronto
Bibliographic record
Abstract
[Introduction]: "Butterfly (Asian and Migrant Sex Worker Support Network) provides support to, and advocates for the rights of, Asian and migrant sex workers. We are an organization founded upon the belief that sex workers are entitled to respect and human rights regardless of type of work, place of origin, or citizenship status. We engage in outreach to sexual service establishments, including apartments, hotels, massage parlours (which can include holistic centres and body rub parlours), and spas across the City of Toronto, meeting with hundreds of migrant Asian sex workers annually. We have found that the majority of the workers in the city’s massage parlours and spas2 are migrant women from Asian countries. They often face unique challenges based both on their work in the sex industry and their immigration status. Such challenges include sex work stigma, racial and gender-based discrimination, substandard working conditions, language barriers, gendered relations of power, surveillance by police, immigration, and bylaw enforcement officers, and precarious living arrangements. Asian sex workers also have to contend with dominant notions that deem them trafficked victims in need of “saving,” thus denying them agency in their decision making.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".