Over 5 Billion Not Served: The TRIPS Compulsory Licensing Export Restriction
Bibliographic record
Abstract
Disease and environmental destruction are two of the most pressing issues facing humankind in the twenty-first century. We rely on technological innovation and its widespread diffusion, to meet these global challenges. But as breakthrough technologies emerge on these fronts, the TRIPS Agreement frustrates product diffusion by prohibiting compulsory licensing to export markets (Article 31(f)). The contribution of this article to the literature on TRIPS compulsory licensing is twofold. First, an economic analysis is employed to argue that Article 31(f) is contrary to the patent and trade rationales embodied in the TRIPS Agreement. Specifically, TRIPS expresses a liability rule that balances the incentive to innovate with trade promotion and technological transfer rationales that justify compulsory licensing in situations of international patent abuse. Provided the incentive to innovate is maintained, serious product diffusion shortfalls in export markets can and should be remedied by compulsory licensing. Second, the article probes specific incentive and diffusion based factors that should be relevant to export market compulsory licensing. Business reasons for failing to sell or license on reasonable commercial terms are assessed for legitimacy from an incentive promotion perspective. At the same time, product demand must be assessed to gauge the magnitude of product diffusion-based problems in developing country markets. The article concludes by examining exhaustion rules and trade diversion concerns that would be central to a regime of international compulsory licensing.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".