Bibliographic record
Abstract
Sexuality has become medicalized and pharmaceuticalized, and one of the more vocal sources of critique against this comes from work at the intersection of feminist science studies and medical sociology. Much of this work has a distinctive activist stream. Big Pharma, Women and the Labour of Love by Thea Cacchioni is both another brick in the wall of resistance to pharma’s definition of normal, ‘healthy’ sex and an interesting study in the way heterosexual norms, the coital imperative and penetrative sex prevail through the health care system to the individual. Cacchioni’s study gives the reader clear, strong and empirically grounded examples of how this is discursively done by and to individuals as they learn to enact (or occasionally resist) the labour of love. Thea Cacchioni is an assistant professor in the Department of Women’s Studies at the University of Victoria, Canada. In addition to academic research on heterosexuality, sexual pain, and the medicalizaion of sexuality, she has also testified against ‘pink Viagra’ before the US Food and Drug Administration and worked closely with academic and activist Leona Tiefer in the New View Campaign. The research in this book is another challenge to contemporary trends in sexual medicalization, built around qualitative data, with careful analysis of in-depth interviews, and framed in a critique of health care’s pharmaceuticalization practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.047 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".