Pathways and Policies to (Bio) Pharmaceutical Innovation Systems in Developing Countries
Bibliographic record
Abstract
Developing countries have traditionally been regarded as users of technology developed abroad. During the 1980s and 1990s this approach to meeting domestic healthcare needs faced new barriers to consumption and use that resulted from the high cost of drugs and the emergence of new international trade, investment and intellectual property rules. Attention was thus drawn to the possibility of building (bio)pharmaceutical innovation systems at home. By examining the experiences of India, Cuba, Iran, Taiwan, Egypt and Nigeria, this paper identifies a multiplicity of pathways for doing so. Because innovation is embedded in both a policy and institutional context, country‐specific triggers and drivers of innovation processes have been important. None the less, some commonalities do appear. Among the more notable triggers were the existence of healthcare crises and earlier incentives that had focused the attention of critical actors on domestic healthcare problems and stimulated a conscious effort by firms to master technology. The interactivity among four types of policies—those strengthening the knowledge base, stimulating capacity building, opening space for local firms and creating incentives for innovation were important in shaping the way these triggers were perceived and in driving the subsequent innovation process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".