Bias and science in knowledge production: implications for the politics of drug regulation
Bibliographic record
Abstract
Public concerns about the regulation of the pharmaceutical industry have intensified in recent years, not least because of a series of controversies about drugs such as those used in the treatment of depression, arthritis, and AIDS. Paradoxically, these concerns centre on the over-consumption of medicines of dubious benefit in Western societies, and lack of access to essential medicines in the Global South. Central questions that are explored include: what are the implications for health of existing systems of pharmaceutical drug regulation?; and what do existing systems of drug regulation reveal about the power of transnational pharmaceutical corporations to shape regulatory and other policies? The importance attached to considering the Irish regulatory system in its international context is reflected in the inclusion of chapters that address the implications of World Trade Organisation and EU regulatory policies and regulatory trends in Canada, Britain and Australia. By demonstrating how the analysis of pharmaceutical drug regulation can provide rich insights into the operation of power in contemporary society, this book challenges the prevailing construction of drug regulation as a sphere of ‘policy without politics’ and aims to contribute to the imagination of better ways of regulating medicines.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".