Governing female sexuality: Prostitution, problematic associations and the subcommittee on solicitation laws.
Bibliographic record
Abstract
Fuelled by the murder and disappearance of sex workers in British Columbia, the Subcommittee on Solicitation Laws (SSLR) was enacted to review current solicitation laws and recommend changes to ensure the safety of sex workers and the communities in which they work. Discourses of prostitution used by the SSLR were analyzed using governmentality literature (Rose, 1999) and Fairclough's (1992) social theory of discourse, to determine their continuity and variability from existing prostitution discourses, as well as their embodiment within the problematic of female sexuality. Although prostitution is not illegal in Canada, associations with crime, violence and public nuisance, serve to problematize prostitution and render it governable. It was found that discourses of prostitution used by the SSLR were similar to those of the previous Canadian governmental committees. This analysis also documents the shift from the problematization of prostitution (protectionist rationalities) to the problematization of the governance of prostitution (neo-liberal rationalities).Dept. of Sociology and Anthropology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .M37. Source: Masters Abstracts International, Volume: 45-01, page: 0161. Thesis (M.A.)--University of Windsor (Canada), 2006.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".