Fallen Women and Rescued Girls: Social Stigma and Media Narratives of the Sex Industry in Victoria, B.C., from 1980 to 2005
Bibliographic record
Abstract
Cet article compare les portraits médiatiques de personnes qui travaillent dans l'industrie du sexe avec les représentations de soi de ces travailleurs, représentations qui incluent leurs origines personnelles et leurs expériences vécues au quotidien. Notre objectif est de jauger la distance empirique entre les descriptions médiatiques et la réalité vécue de ces travailleurs puis de comprendre comment les médias contribuent à cons-truire, reproduire et approfondir les stigmates sociaux associés au travail dans l'industrie du sexe. Nous avangons que le fait de distinguer la variabilité historique et spatiale de ces stigmates ainsi que le fait d'expli-quer leurs racines dans les activités de sens et de pratiques des auteurs et autorités médiatiques représentent une avancée cruciale pour la compréhension de leur construction sociale. This paper compares media portrayals of people who work in the sex industry with these workers' self-reports of their personal backgrounds and experiences of what they do for a living. Our aim is to first, gauge the empirical distance between media depictions and workers' lived reality, and second, to understand how the media contributes to constructing, reproducing and deepening the social stigmas associated with working in the sex industry. We argue that pulling apart the historical and spatial variability of these stigmas and explicating their roots in the meaning-making activities of media authors and authorities is a crucial step towards understanding their social construction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".