Bibliographic record
Abstract
Purpose The majority of the world's population has limited access to needed medicines. The purpose of this paper is to explain why certain characteristics of the global pharmaceutical market have not served a large majority of potential consumers in the developing world. Design/methodology/approach Through a political economy analysis of evolutionary and regulatory aspects of both supply and demand conditions for global pharmaceuticals, it can be understood why most of the world's poor have limited access to basic medicines. The paper then turns to what avenues are available for improving access to medicines. An analysis of the chief proposed solutions, namely: pooled demand and relaxation of intellectual property rights, reveals their inadequacies. A third emerging avenue, the growing production of pharmaceuticals in the south, is examined through case studies of leading producers including India, China, and South America. Findings While each of the three options offers potential benefits, none is adequate to solve the problem – a new, perhaps combinatorial, approach will be needed to ensure that a wider global market for pharmaceuticals can be created. Originality/value The paper offers insights into the political economy of the global pharmaceutical industry.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".