Moving Theory into Practice: Human Rights Impact Assessments of Intellectual Property Rights in Trade Agreements
Bibliographic record
Abstract
This article explores the development of methodologies for human rights and right to health-specific impact assessment (RTHIA) of trade-related intellectual property rights. These methodologies seek to respond to the restrictive impact of international and bilateral trade rules on domestic and global policy options to ensure access to affordable medicines in low and middle-income countries. RTHIA methodologies are emerging from human rights impact assessments, themselves an offshoot from the broader field of social and health impact assessment. A right to health specific impact assessment allows policy makers to prospectively predict the impact of intellectual property rights on domestic medicines policy, and ergo on the realization of legal duties under the international human right to the highest attainable standard of health. The effective implementation of such an assessment provides an evidence base for broadening policy space in these countries towards improving access to generic and affordable patented medicines. Yet there has been little consensus to date on key questions of principle, methodology and implementation. We overview current literature and practice in this regard in order to assess the current state of the field and the prospects for wider-scale implementation. We first assess the growing international focus on the impact of trade-related intellectual property rights on access to medicines. We then explore the emergence of impact assessments in relation to health and human rights. Finally, we analyse the practical, methodological, political and theoretical challenges of RTHIA, and overview developments in practice and scholarship that suggest effective responses to these challenges.
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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 | 0.115 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.004 | 0.053 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".