Bibliographic record
Abstract
Since the late 1980s the global intellectual property rights (IPR) system has been strengthening dramatically as much of the developing world introduces patent protection for new drug products. This may lead to more research on drugs to address developing country needs. As there are identifiable differences in the drug demands of these countries as compared to those already offering such protection the situation offers a unique opportunity to examine the incentive role of patent protection. We use new survey data from India, the results of interviews with industry, government and multinational institutions, and measures of R&D activity constructed from a variety of statistical sources to determine trends in the allocation of research to products specific to developing country markets. There is some, although limited, evidence of an increase in the mid- to late 1980s which appears to have leveled off in the 1990s. In interpreting the trends we examine factors that might enhance, or dampen, a firm's responsiveness to the availability of product patents. The picture presented here provides a baseline' against which future research activity can be compared once the new global patent regime is fully established and uncertainty about its implementation is resolved.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".