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Record W2032248169 · doi:10.1177/1464700113483242

Affective sex: Beauty, race and nation in the sex industry

2013· article· en· W2032248169 on OpenAlexaff
Megan Rivers‐Moore

Bibliographic record

VenueFeminist Theory · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBeautyGender studiesContext (archaeology)TourismAttractivenessRace (biology)SociologyMythologySex workWhite (mutation)ExoticismValue (mathematics)Physical attractivenessAestheticsSocial psychologyPsychologyPolitical scienceGeographyArt

Abstract

fetched live from OpenAlex

This article considers the role of beauty in Costa Rican sex work. In the context of sex tourism, beauty operates as affective labour performed by sex workers, labour that is mediated by deeply contradictory understandings of race and nation. Theorising beauty as a form of affective labour means thinking about beauty as value, as something that circulates, can be exchanged and is ultimately relational. While Costa Rica's national mythology has long focused on claims to white origins, sex tourists identify local women's ‘exoticism’ and non-whiteness as particularly appealing. Commercial sex does not simply depend on an imperial male gaze, but rather requires us to consider how particular national mythologies about race are linked to ideals of attractiveness in complex ways. I also explore how women experience and manage their sexual attractiveness to foreign tourists in their daily lives and work.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.012
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.013
GPT teacher head0.289
Teacher spread0.276 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations28
Published2013
Admission routes1
Has abstractyes

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