Emergent Patterns in the Regulation of Pharmaceuticals: Institutions and Interests in the United States, Canada, Britain, and France
Bibliographic record
Abstract
Although industrialized nations regulate pharmaceuticals to ensure their safety and efficacy, they balance these concerns with those related to the timeliness of the approval process and the burdens involved in meeting regulatory criteria. The United States, Canada, Britain, and France have adopted different approaches to the regulation of pharmaceuticals that place varying emphases on these competing goals and involve the participation of private interests to different extents. The regulatory approval processes and the government-industry relationships inherent within them are compared in the United States, Canada, Britain, and France by analyzing five features that distinguish the U.S. pluralist from the European corporatist approaches to policy development: representation (internal versus external), process (closed versus open), stance (informal, accommodative versus formal, adversarial), institutional power (fragmented versus centralized), and resources. An institutional framework further characterizes these approaches as based on models of managerial discretion and adjudication (United States), consultation (Canada), and bargaining (Britain, France) to clarify the patterns that emerge. While the approach that most effectively supports product safety involves managerial discretion as occurs in the United States, formal mechanisms for negotiation might be incorporated rather than a reliance on the judicial process. In an era of globalization and regulatory harmonization such divergence has significant implications. First, where harmonization in Europe involves the mutual recognition of one country's product licensing decision by the others, differences in evaluative processes remain important. Second, as harmonization leads to a common set of regulatory criteria, the criteria adopted tend to be those of nations with the least stringent regulatory standards, making evident the need for more responsive systems of post-market surveillance to protect the public interest.
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
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".