Bibliographic record
Abstract
Before a new drug can be marketed the Food and Drug Administration must be satisfied that it is safe and effective. According to conventional wisdom, the cost and delay involved in this process diminish the incentives to invest in the development of new drugs. Accordingly, several reforms aimed at restoring such incentives have been implemented and others have been advocated.This paper challenges the central argument in the debate on the topic, namely that drug regulation and drug innovation are necessarily at odds with each other. Although intuitively appealing, the argument that drug regulation negatively affects the incentives to innovate does not fully capture the role that regulation plays in this industry. This paper shows that the regulatory framework is not solely a burden imposed on the industry; it also provides a valuable service to the industry.Specifically, drug regulation provides certification of drug quality. Such certification, which may not be easily achieved by private market-based mechanisms, prevents the market from becoming a market for lemons. Therefore, rather than decreasing the expected returns to innovation, this aspect of regulation contributes to the value of new drugs and may actually encourage innovation. This point has largely been absent from most cost-benefit analyses of drug regulation, yet without it any discussion of the merits of regulation is incomplete.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.016 | 0.010 |
| 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, 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".