An opportunity to remove harmful intellectual property provisions from the Comprehensive and Progressive Agreement for Trans-Pacific Partnership
Bibliographic record
Abstract
Legal provisions in trade agreements, including those related to intellectual property (IP), can impede access to medicines. The 12-party Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is currently undergoing a review. This provides an opportunity to update the CPTPP's Intellectual Property Chapter to remove certain provisions that were negotiated in the context of its precursor, the Trans Pacific Partnership (TPP), many of which have been suspended. These include several 'TRIPS-Plus' provisions - IP provisions exceeding the requirements of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS). This paper reviews the CPTPP's TRIPS-Plus provisions, including those suspended and those still in place, and argues for their removal based on evidence of their likely effects on medicines access and recent changes in the political environment. Since the CPTPP was signed in 2018, accumulated evidence has demonstrated that TRIPS-Plus provisions negatively impact access to medicines. Lack of access to COVID-19 medical products in low- and middle-income countries has highlighted major problems with TRIPS. Furthermore, the US has diverged from a TRIPS-Plus agenda, rendering the suspended provisions obsolete. Removing the CPTPP's TRIPS-Plus provisions, while challenging, would preserve Parties' policy flexibility to design their laws in ways that protect access to medicines.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".