Affordability versus innovation: Is compulsory licensing the solution?
Bibliographic record
Abstract
In an era which marks an exceptional phase of growth in science and technology, the acute disparities in access to healthcare still persist. So where on one hand scientific advancement in medicine aims at increasing life expectancy, on the other hand there are millions who are denied access to existing medicines. Patents on medicines also pose a significant barrier to access new drugs, especially in low and middle income countries which already suffer from poor health financing mechanisms. The patent laws were built on the assumption of incentivizing the innovators by rewarding them with the exclusive right to produce, sell or market the innovation. The basic premise for granting patents was based on the thought that it would increase investment in research and development promoting dynamic gains through newer innovations. However, evidence found to support this justification is meager. So in a situation where the drug gap still persists and we aim to achieve sustainable development goals by 2030, this paper attempts to focus on understanding how compulsory licensing has been used in selected cases to alleviate the major legal and political barriers to access medicines. The methodology comprises of cross-country comparison of patent framework and compulsory licensing cases. The sample selected for study includes both developed as well as developing countries. The aim is to evaluate the policy approaches used by selected countries to grant compulsory licenses and to identify the best practices for evidence-based policy making on international issues related to pharmaceutical patents. In each case, a driving factor has been the international extension of patent laws through trade agreements; first bilaterally (US-Canada) and subsequently internationally (1995 Uruguay round, under which low- and middle-income countries were granted a grace period until 2005 to comply).
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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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".