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Record W7006894872

When Event Spaces & Commercialised Sex Spaces Overlap: Gendered Discourses of Sex Work & the Olympic Games

2018· other· en· W7006894872 on OpenAlexaboutno aff

Bibliographic record

VenueQueen Margaret University Publications Repository (Queen Margaret University) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSex workSex workersPoliticsEvent (particle physics)Work (physics)Scope (computer science)Space (punctuation)Perspective (graphical)Public space
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.005
Science and technology studies0.0020.004
Scholarly communication0.0010.001
Open science0.0060.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.018
GPT teacher head0.234
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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