Doing Gender Well and Differently in Dirty Work: The Case of Exotic Dancing
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.026 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".