Taint: an examination of the lived experiences of stigma and its lingering effects for eight sex industry experts
Bibliographic record
Abstract
As part of a larger study examining the effects of the design of the off-street sex industry on sex worker's health and safety practices, eight sex work experts who had experience as sex workers and as advocates and service providers were interviewed to garner their community engagement expertise in shaping the research. During narrative interviews, these experts discussed how stigma influenced their personal lives and their social justice work among sex workers. Their insights into stigma are unique to the literature because our experts simultaneously confronted direct instances of stigma that were a part of their personal and professional lives, sometimes concealing their sex work histories during the course of their professional support and advocacy work. As a result of this concealment, and because of how sex workers are sometimes mistreated, experts experienced stigma vicariously (indirectly) when their own sex work histories were not apparent. As a result of these experiences, participants became proficient at managing discrediting information about themselves when in the presence of those they mistrusted. They supported sex workers through stigmatising ordeals by using knowledge gained from these intersecting direct and vicarious experiences stigma, continuously building capacity within themselves and among other sex workers to resist stigma.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| 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".