Bibliographic record
Abstract
With the advent of HIV/AIDS “crisis” during the 1980s female sex workers were viewed as “high risk” group for spreading AIDS in the general population (read: white hetero-sexual men and their “innocent” wives and children). Given that prostitution has long been perceived as a threat to public health a medical model of disease made a particularly compelling connection between infection and sex work which conflated sexual activity with disease transmission adopted a risk-based intervention approach that pathologized sex work and scapegoated sex workers (Meaghan 1989). Rather than enabling sex workers to minimize health risks while working many policy-makers and law enforcement agents have attempted to eradicate or regulate commercial sexual encounters by requiring sex workers to register to be confined to segregated areas and to be inspected and quarantined if found to be infected (Morgan Thomas; Canadian Organization for the Rights of Prostitutes). Media accounts suggested that those who performed this kind of work were “reservoirs of infection” and blamed sex workers for disease and disorder in society (Aralt and Wasserheit 33). State policies and practices dramatically increased legal moral and social censure of sex workers while failing to deal with the real risks posed to them by clients (Bastow; Brock 1989; Brock 1998). Rarely in these cultural discourses were sex workers perceived as possessing a specialized knowledge concerning sexual agency and safer sex practices in HIV/AIDS prevention. (excerpt)
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".