Reagan, Regulation, and the FDA: The US Food and Drug Administration's Response to HIV/AIDS, 1980-90
Bibliographic record
Abstract
The beginning of the AIDS epidemic in the 1980s placed tremendous strain on the Food and Drug Administration, regulator of one quarter of the domestic US economy and protector of the nation's drug supply. Much has already been written on the AIDS epidemic, and this body of literature continues to expand, but careful consideration of this subject matter is of crucial significance to understanding the interaction between regulatory agencies, the executive branch, industry, and the public. Periodically, the FDA has gone through phases in which various priorities dominated. At one point or another it has distinguished itself as a regulatory agency, law enforcement agency, and science agency, depending on the political party in power, the ideology of the FDA Commissioner, and the influence of external stakeholders. The AIDS epidemic, which gave rise to a new and robust coalition of AIDS activists, reformers, and libertarians, tested the agency's institutional identity. To some commentators, the FDA’s management of the AIDS crisis was a serious blunder, akin to the Reagan administration’s failure. This paper contends, however, that despite the absence of presidential leadership, the FDA succeeded in rising to the challenge of the AIDS epidemic in the 1980s by refashioning its existing regulatory rules, reaching out to the AIDS movement and the pharmaceutical industry, and maintaining its strong commitment to consumer protection.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".