Bibliographic record
Abstract
Abstract Thirty years ago, the only people drug companies thought worth buying were doctors and politicians. But the ground began to shift in the 1980s, when HIV/AIDS activists showed everyone how powerful patient advocates could be. It didn't hurt that many advocates were so strapped for money that they could be purchased at bargain prices. Today over 80 percent of patient advocacy groups accept money from the pharmaceutical industry, and the testimony of marginalized patients carries such cultural power that drug companies like Sprout Pharmaceuticals are willing to fake grassroots patient movements. Whether you see this change as a victory for patients or a cautionary tale of institutional corruption depends on how deeply committed you are to the idea that free markets represent our best hope for the future of medicine. Count Sharon Batt as one of the skeptics. Her superb new book, Health Advocacy, Inc.: How Pharmaceutical Funding Changed the Breast Cancer Movement, is a deep scholarly account of the way that pharmaceutical funding has warped the patient advocacy movement into a tool for medical capitalism. Taking the Canadian breast cancer movement as a case study, Batt uses extensive interviews, scholarly resources, and her personal history to explore the struggles faced by patient advocates as they decide whether industry funding is necessary to keep their organizations afloat.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".