Stigma Hierarchies: The Internal Dynamics of Stigmatization in the Sex Work Occupation
Bibliographic record
Abstract
Scholars studying stigmatized, or “dirty work,” occupations have tended to characterize people outside of the occupation as the stigmatizers and those within the occupation as social supports who buffer each other from stigma. We argue that this characterization discounts the unique ways stigmatization can take place within heterogeneous occupations and the challenges it raises for finding support from other occupational members. Based on a six-year qualitative study of the sex work occupation in Canada, we explore the internal dynamics of stigmatization in the occupation. Our analysis reveals that sex workers are not just the stigmatized but also the stigmatizers, as they elaborate, borrow, and adapt perceptions of stigma to rank and place each other into a stigma hierarchy. To avoid the risks of being stigmatized based on this hierarchy, sex workers engage in stealth organizing to find safe others within the occupation to provide social support. Thus the occupation is not a stigma-free safe haven for its workers. Instead, the occupation as a whole is characterized by dissension among its members. Their efforts to find social support lead to what we call bounded entitativity: a sense of being grouplike that is confined to small community groups within a broader occupational context of dissension. We found bounded entitativity to be associated with challenges for occupational members in undertaking social change efforts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".