Sex, drugs, and rhetoric: The case of flibanserin for ‘female sexual dysfunction’
Bibliographic record
Abstract
In August, 2015, the US Food and Drug Administration approved Addyi (flibanserin) for the treatment of Hypoactive Sexual Desire Disorder in premenopausal women. Ten months before that, the FDA had held a Patient-Focused Drug Development Public Meeting to address the 'unmet need' for a pharmaceutical to treat that condition. I attended that meeting as a rhetorical observer. This essay is an account of persuasive strategies used on, and then by, the FDA, as it considered approving a drug that was not convincingly either safe or effective. The essay turns on three texts: the 'Even the Score' pro-drug campaign that informed the patient-focused meeting, the text of the meeting itself, and the FDA's own published report of the event. I describe how a pharmaceutical company (Sprout, then owners of flibanserin) recruited, and then ventriloquized, both health professionals and members of the public to pressure the FDA to approve a sex drug for women - claiming that not to do so was evidence of sexism. I argue, with rhetorical evidence, that the case for approving flibanserin had already been won before Sprout submitted its application.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.040 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.016 | 0.013 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".