Lessons learned: a framework methodology for human rights impact assessment of intellectual property protections in trade agreements
Bibliographic record
Abstract
Currently, two billion people lack regular access to essential medicines in contradiction of their right to health under international law. With the rapid growth of intellectual property provisions in international trade agreements in recent years, governments are increasingly bound to provide stringent patent protection to pharmaceuticals, resulting in higher drug prices, which exacerbate the inaccessibility of medicines. As a result, there is a growing consensus in human rights and public health communities that policy-makers should ensure that trade agreements do not negatively affect the right to health, and moreover that human rights impact assessment offers a pragmatic and increasingly well-considered framework for achieving this aim. Drawing on numerous case studies and international human rights standards, this article proposes a pragmatic framework methodology for non-governmental organizations to carry out human rights impact assessment of trade-related intellectual property protections as part of their advocacy campaigns.
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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 | 0.077 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".