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Doing Gender Well and Differently in Dirty Work: The Case of Exotic Dancing

2011· article· en· W2144444215 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGender Work and Organization · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsMount Allison University
Fundersnot available
KeywordsFemininityWork (physics)Stigma (botany)MasculinityGender studiesSex workSocial psychologyPsychologySociologyEngineeringMedicine

Abstract

fetched live from OpenAlex

This article explores how a group of exotic dancers do gender and manage the stigma associated with their work and identities. We draw upon stigma management strategies from the dirty work literature and illuminate the doing of gender in these strategies. We also contribute to the debate that gender can be done well and differently through simultaneous, multiple enactments of femininity and masculinity. We consider the experiences of 21 exotic dancers working in a chain of UK exotic dancing clubs and conclude that in order to be good at their job, exotic dancers are expected to do gender well, that is, perform exaggerated expressions of femininity. However, we also theorize that for some dirty workers, specifically exotic dancers as sex workers, doing gender well will not be enough to reposition bad girls (bad, dirty work) into good girls (good, clean work). Finally, we propose that doing gender well will have different consequences in different types of work, thereby extending our findings to other dirty work occupations and organizations in general.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.246
Teacher spread0.196 · 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