Sex, Lies and Pharmaceuticals: How Drug Companies Plan to Profit from Female Sexual Dysfunction
Bibliographic record
Abstract
S ex, Lies and Pharmaceuticals (Moynihan & Mintzes, 2010) is sub- titled "How drug companies plan to profit from female sexual dysfunction."Unlike the drug companies, the book delivers on its labelled claims.Using the techniques of investigative journalism, Australian journalist Ray Moynihan teams up with medical science expert Barbara Mintzes, a researcher in the Department of Pharmacology and Therapeutics at the University of British Columbia.Together, they deftly follow a paper trail to empirically establish the social construction of a single contemporary disease state.In painstaking detail that names names, the authors document the political struggle to realize and resist the acceptance of FSD into current medical wisdom.To this end, the book's narrative structure follows the journalism adage "show, don't tell."The reader isn't required to accept the claims made based on the professional authority of the authors; nor is an in-depth scholarly analysis included to guide the reader to an explicit thesis.The facts are left to speak for themselves; yet, the book weaves a narrative plot out of facts mined from an extensive archive of interviews, events, and other institutional, scientific, and biographical texts.The result is a damning critical treatise that warns of the public danger when four elite social worlds intersect:• the world of corporate marketing and public relations, with its creation and distribution of salient messages to wider publics through media editorial and advertising;Reviews
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".