The Labour of Paying for Education: An Exploration of Student Sex Work in Canada
Bibliographic record
Abstract
This thesis contributes to a small body of research that examines student sex work in aCanadian context.By drawing on data gathered from semi-structured qualitative interviews with ten student sex workers, this thesis seeks to gain a nuanced understanding of students' experiences with sex work in Canada, and their use of university support services.Specifically, this study explores student sex workers' motivations for entering the sex industry, the benefits and challenges that they have encountered while working, whether they are accessing university support services and if and how they would benefit from the implementation of targeted support services in post-secondary institutions.The findings reveal that student sex workers in Canada have a variety of reasons for entering the sex industry, among which financial incentives, psychological benefits and flexibility appear to be paramount.Further, this research makes it evident that while student sex workers do have unique concerns that dissuade them from accessing university support services, many of their concerns reflect issues affecting all postsecondary students.In order to fill the gaps in existing service provision, this research seeks to provide suggestions as to how services could be implemented and adapted to better meet the needs of the student sex-working population.To my outstanding committee -Professor Ummni Khan and Professor Lara Karaian, thank you.Ummni, my internal examiner, thank you for your invaluable suggestions and edits.You have offered insightful advice throughout the duration of this project.Lara, my external examiner, I am grateful for your time, efforts, and the many important and productive critiques you have offered
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.038 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".