“Nobody's Ever Going to Make a Fag<i>Pretty Woman</i>: Stigma Awareness and the Putative Effects of Stigma Among a Sample of Canadian Male Sex Workers
Bibliographic record
Abstract
The purpose of this study was to examine male sex workers' awareness of the social stigma surrounding involvement in the sex industry and the possible effects of that stigma. Personal interviews were conducted with 21 men (9 independent escorts who advertised via the Internet and 12 escorts/erotic masseurs who were on contract with an agency). Results indicated that a majority of interviewees believed sex work was stigmatized but attributed this stigma to society's tendency to conflate escort/erotic masseur with street-based prostitute and society's negative view of human sexuality in general and homosexuality in particular. It should be noted that interviewees did not necessarily perceive the gay community as more tolerant than the heterosexual community of persons involved in the male sex industry. In terms of how participants saw the sex trade, both prior to and during their involvement, multifarious viewpoints emerged (i.e., some engaged in "whore mythologizing" while others reported having no clearly defined perception of male sex workers). Finally, results suggested that some participants believed their involvement in a stigmatized industry was deleterious to them personally whereas others maintained that the consequences of being an escort/ erotic masseur were largely positive.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".