When Event Spaces & Commercialised Sex Spaces Overlap: Gendered Discourses of Sex Work & the Olympic Games
Bibliographic record
Abstract
In the past decade, debates regarding the sex industry, especially street-level sex work, have become exacerbated by the hosting of global sporting events. Such issues as displacement, safety concerns and financial cuts to social services have contributed to the problematisation of the overlap between mega event spaces and commercial sex spaces. The different approaches that destination cities have implemented to address these aspects of the urban environment reflect the political and economic geographies of sex work and the post-colonial perspective of sex worker as criminal or victim (Agustin, 2008; Hubbard, 1998). This research focuses on a case study of Vancouver as host of the 2010 Winter Olympic Games and examines the situation when commercial sex spaces become event spaces. Qualitative research methods have been conducted in the form of in-depth, semi-structured interviews with city officials, police, former sex workers, academics, NGO's and women's charities. The landscape of the sex industry in Vancouver is analysed in an effort to illustrate the impacts that the preparations for the Olympic Games has on the urban environment. Gendered discourses concerning sex workers' rights to the city and how debates regarding criminalisation of demand/legalisation of sex work are linked to constructions of public space are also analysed (Doezema, 2001; Farley, 2003; Hubbard, 2001; Kempadoo, 2003). There is scope from the findings of this research to inform the dynamics of inclusion/exclusion in diverse European contexts, as more and more cities and countries bid for and host large-scale events.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.041 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".