Attitudes toward sex work among young women in Canadian universities: A complex landscape
Bibliographic record
Abstract
Current research suggests that women students may increasingly turn to sex work to help finance their education due to increased economic demands and its glamourization in the media. To date, no research has empirically examined the influence of societal factors, such as the proliferation of digital technology, as factors increasing positive attitudes toward sex work. Addressing this gap, this exploratory study investigated whether women’s attitudes varied based on the context and venue of sex work. Additionally, the authors sought to enhance the understanding of established factors linked to attitudes toward sex work. One hundred fifty women-identified students completed an online survey with a within-subjects design to measure their attitudes toward five different types of sex work varying in level and type of contact from street level (in person/full contact) to webcamming (internet-mediated/no contact). In general, women students had negative attitudes toward sex work but held mildly positive attitudes regarding the activity/potency of sex work and, potentially, the women who engage in it. More positive attitudes were held toward sex work when women could maintain a “distance” between themselves and the client, either through a lack of direct genital contact or through digital technology. This research offers a detailed examination of university women’s attitudes toward various forms of sex work, uncovering valuable insights into societal perceptions and how these attitudes vary depending on the context and location of sex work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".