The Trans Pacific Partnership Agreement, intellectual property and medicines: Differential outcomes for developed and developing countries
Bibliographic record
Abstract
The final text of the Trans Pacific Partnership Agreement (TPP), agreed between the 12 negotiating countries in 2016, included a suite of intellectual property provisions intended to expand and extend pharmaceutical company exclusivities on medicines. It drew wide criticism for including such provisions in an agreement that involved developing countries (Vietnam, Peru, Malaysia, Mexico, Chile and Brunei Darussalam) because of the effect on delaying the introduction of low-cost generics. While developing nations negotiated transition periods for implementing some obligations, all parties would have eventually been expected to meet the same standards had the TPP come into force. While the TPP has stalled following US withdrawal, there are moves by some of the remaining countries to reinvigorate the agreement without the United States. The proponents may seek to retain as much as possible of the original text in the hope that the United States will re-join the accord in future. This article presents a comparative analysis of the impact the final 2016 TPP intellectual property chapter could be expected to have (if implemented in its current form) on the intellectual property laws and regulatory regimes for medicines in the TPP countries. Drawing on the published literature, it traces the likely impact on access to medicines. It focuses particularly on the differential impact on regulatory frameworks for developed and developing nations (in terms of whether or not legislative action would have been required to implement the agreement). The article also explores the political and economic dynamics that contributed to these differential outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".