Jeremy A. Greene, Flurin Condrau, and Elizabeth Siegel Watkins, eds. Therapeutic Revolutions: Pharmaceuticals and Social Change in the Twentieth Century
Bibliographic record
Abstract
In 1922, fourteen-year-old diabetic Leonard Thompson received the first injections of insulin at Toronto General Hospital. The medication was life-saving and meant that juvenile diabetes (now called type-1 diabetes) was no longer a death sentence. As long as patients had access to insulin, they could live relatively normal, healthy lives. A genuine therapeutic revolution, perhaps. Yet, a century later, rates of type-2 diabetes have mushroomed and now constitute ninety percent of diabetes cases. Try as they might, pharmaceutical companies have no magic bullet for type-2 diabetes, leaving lifestyle adjustments as the primary intervention. So, what story is the most telling? Which narrative conveys most about how to tackle disease in the twenty-first century? These are just some of the themes explored in Therapeutic Revolutions, a compelling attempt to unpack exactly what has been revolutionary about modern medicine. The volume’s chapters combine history, anthropology, sociology, and science studies, including a number of contributors who work at the intersection of medicine and social science. At a time when the term “interdisciplinary” is wheeled out in academia almost as often as the term “revolutionary,” the editors should be commended for putting together a volume that is much more than the sum of its parts. In that spirit, rather than commenting on each chapter, I will focus on some of the book’s key themes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".