The Interpretation of the Flexibilities in the <i>TRIPS Agreement</i> in Light of the Right to Health
Bibliographic record
Abstract
This article explores the interplay between the flexibilities embedded in the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) and the international human right to health, particularly in the context of access to affordable medicines. It critically examines how TRIPS provisions —especially Articles 7 and 8— can be interpreted in light of evolving human rights obligations under instruments such as the International Covenant on Economic, Social and Cultural Rights and the WHO Constitution . The analysis draws on the Doha Declaration and relevant jurisprudence to argue for a dynamic, evolutive interpretation of TRIPS flexibilities, including compulsory licensing, government use, and exhaustion regimes, emphasizing their potential to reconcile intellectual property protection with public health objectives. Furthermore, the article assesses the role of both state and corporate actors in realizing the right to health, considering recent legal developments such as the EU Corporate Sustainability Due Diligence Directive . It contends that fair pricing strategies and enhanced global cooperation are critical to ensuring equitable access to medicines, especially in light of global health emergencies and rising drug costs. Ultimately, the study advocates for a tailored, context-sensitive application of TRIPS flexibilities, aligned with both trade law and human rights law, to support sustainable and inclusive global health governance.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".