Intellectual property and access to medicines: mapping public attitudes toward pharmaceuticals during the United States-Mexico-Canada Agreement (USMCA) negotiation process
Bibliographic record
Abstract
BACKGROUND: Transparency and accountability are essential components at all stages of the trade negotiation process. This study evaluates the extent to which these principles were upheld in the United States' public consultation process during the negotiation of the United States-Mexico-Canada Agreement (USMCA), with respect to public comments about the pharmaceutical sector and access to medicines. RESULTS: The public consultation process occurred before the start of official negotiations and was overseen by the Office of the United States Trade Representative (USTR). It included both written comments and oral testimony about US trade negotiation objectives. Of the written comments that specifically discussed issues relating to pharmaceuticals, the majority were submitted by private individuals, members of the pharmaceutical industry, and civil society organizations. Nearly all comments submitted by non-industry groups indicated that access to medicines was a priority issue in the renegotiated agreement, with specific reference to price affordability. By contrast, more than 50% of submissions received from members or affiliates of the pharmaceutical industry advocated for strengthened pharmaceutical intellectual property rights, greater regulatory data protections, or both. This study reveals mixed outcomes with respect to the level of transparency achieved in the US trade negotiation process. Though input from the public at-large was actively solicited, the extent to which these comments were considered in the content of the final agreement is unclear. A preliminary comparison of the analyzed comments with the USTR's final negotiating objectives and the final text of the USMCA shows that several provisions that were advanced exclusively by the pharmaceutical industry and ultimately adopted in the final agreement were opposed by the majority of non-industry stakeholders. CONCLUSIONS: Negotiators could increase public transparency when choosing to advance one competing trade objective over another by actively providing the public with clear rationales for their negotiation positions, as well as details on how public comments are taken into account to form these rationales. Without greater clarity on these aspects, the public consultation process risks appearing to serve as a cursory government mechanism, lacking in accountability and undermining public trust in both the trade negotiation process and its outcomes.
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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 | 0.028 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".