Patents and Pharmaceutical R&D: Consolidating Private-Public Partnership Approach to Global Public Health Crises
Bibliographic record
Abstract
Intellectual property (IP) is a reward and incentive market-driven mechanism for fostering innovation and creativity. The underlying, but disputed, assumption to this logic is that without IP, the wheel of innovation and inventiveness may grind to a halt or spin at a lower and unhelpful pace. This conventional justification of IP enjoys, perhaps, greater empirical credibility with the patent regime than with other regimes. Despite the inconclusive role of patents as a stimulant for research and development (R&D), special exception is given to patent’s positive impact on innovation and inventiveness in the pharmaceutical sector. This article focuses on that sector and links the palpable disconnect between the current pharmaceutical R&D agenda and global public health crises, especially access to drugs for needy populations, to a flaw in the reward and incentive theory of the patent system. It proposes a creative access model to the benefits of pharmaceutical research by pointing in the direction of a global treaty to empower and institutionalize private-public partnerships in health care provisions. Such a regime would restore balance in the global IP system that presently undermines the public-regarding considerations in IP jurisprudence.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".