Sex trade workers in Halifax Nova Scotia: What are their risks of HIV at work and at home?
Bibliographic record
Abstract
Women who work as prostitutes are typically viewed as “victims” of pimps or powerful clients or as drug-addicted women with little if any control over their lives. Such characterizations fail to capture the complexities of the women’s lives and do not account for the diversity of women who work in the sex trade industry both locally as well as more globally (Day; Hancock; Wong Tam Ho Lim Lim Wan and Chan). Moreover such depictions fail to view the women as active agents within their relations with men as well as others. It is true that there are strong structural constraints shaping female prostitutes’ practices including their ability to use condoms with clients but at the same time the women are not totally helpless as many researchers are beginning to document (Campbell 2000; Wojcicki and Malala). This article will seek to show the ways in which structural forces (e.g. gendered power relations and economic inequities) shape and influence female prostitutes’ ability to negotiate and use condoms while working. We will also attempt to show that the women are in certain circumstances active agents in the use of condoms. Indeed we follow Wojciki and Malala in suggesting that there are structural issues influencing condom use but there are also choices that can be made within particular situations and women are not totally powerless but have agency. (excerpt)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".