Bibliographic record
Abstract
THIS BOOK IS ABOUT MANIFESTATIONS of power in medicines and pharmaceutical industry policy. The main focus is on the Republic of Ireland but there are chapters also on drug regulation in Canada, Britain and Australia. The multinational pharma companies loom larger in Ireland than in most other countries; several chapters detail the implications for this small country of the presence of a major cluster of global drug companies. Globalisation is the hallmark of the drug sector; innovation and production occur within international networks which are mirrored by interaction between regulatory agencies which operate similar systems of control and monitoring. Since the 1990s, many aspects of product safety regulation have been standardised across the developed countries through the International Conference on Harmonization (ICH) process, sponsored by the regulatory agencies and industry associations of the USA, the European Union and Japan. While orchestrating vast scientific, economic and technological resources, the big pharma companies participate as insiders in national policy processes, such as those described in this book. Firms typically affirm a commitment to the health and economic concerns of the local jurisdiction ? however governments cannot help but be sensitive to their global reach and power to choose where to invest.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".