“Stand With Us”: Asian and Migrant Sex Workers Must Not Be Forgotten in Our Response to the COVID-19 Pandemic and Anti- Asian Racism
Bibliographic record
Abstract
People all over the world have been affected by the COVID-19 pandemic. However, racialized, migrant, poor, criminalized, and otherwise marginalized people, including sex workers, have been disproportionately affected. The COVID-19 pandemic has exposed the inequality Asian sex workers continuously experience and how they have fallen through the cracks. The heavy economic, social, and mental impacts on them during the pandemic have exacerbated their exclusion from access to financial relief, social support, and health services. The stigma, discrimination, poverty, violence, harassment, surveillance, and repressive policing have also been intensified by govern- ment emergency measures. Despite the oppression and the challenges, sex workers’ organizations all over the world have spoken out about their struggles, developed rapid responses to support their communities, and asked for support. In this grassroots com- munity report, I illustrate the oppression and challenges Asian and migrant sex workers in Canada faced during the pandemic and examine how one Canadian sex worker organization, Butterfly (Asian and Migrant Sex Workers Support Network), worked with workers, migrants, and racialized communities to support Asians and migrants and to build their resilience. Butterfly is a community-led organization that organizes over 5,000 Asian workers, including permanent residents, refugees, and non-status women, who work in massage parlours and the sex industry across Canada and provides them with crisis, social, health, and legal supports. Butterfly also builds the capacity and leadership of the workers, organizing them to fight for their rights. Butterfly is founded upon the belief that sex workers are entitled to respect and the acknowledgement of their human rights. In addition to the challenges faced by workers, including racism, classism, sexism, gender inequality, xenophobia, transphobia, language barriers, and other kinds of oppression, both undocumented and permitted workers have little to no access to the health and social services needed to navigate their work safely, and they live in constant fear of being deported from Canada. They face surveillance, policing, and criminalization and, particularly, the harms done by the anti-trafficking movement.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.053 | 0.019 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.018 |
| 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".