Bibliographic record
Abstract
Samantha Majic’s Sex Work Politics challenges common assumptions about activist organisations and specifically how activist organisations can both work within policies governing their services and also continue to be spaces of protest and change. Conducting research on California Prostitutes Education Project (CAL-PEP) and St. James Infirmary (SJI), organisations that offer services to sex workers in California, Majic states at the outset, the ‘broad question I explore in this book [is]: how did they develop and sustain themselves as spaces that offer services and support oppositional political stances (in this case, recognizing prostitution as legitimate work)?’ (p. 2). Majic’s exploration of the ways that CAL-PEP and SJI respond to sex worker needs demonstrates the diversity of kinds of sex work, which represents the diversity of kinds and kinks of sexual interests and sexuality. CAL-PEP offers services to both indoor and outdoor sex workers and offers health promotion and HIV prevention information to individuals on the streets or who come to the centre who are not sex workers, but who are marginalised and disadvantaged—and at risk of HIV/AIDS exposure. One of the effects of criminalisation of sex work has been a reluctance (or refusal) of sex workers to disclose what they do, including when seeking health care. Because they can talk openly about what they do at CAL-PEP and SJI, the staff and volunteers can respond directly to their needs, reducing the health risks associated with inappropriate or inadequate medical attention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".