The impact of international trade agreements on health : patent system harmonization and medicines in Mexico
Bibliographic record
Abstract
Patent system harmonization obligations found within international trade agreements have been subject to intense scrutiny over the past two decades due to the potential negative implications for public health in developing countries. In 1994, NAFTA became the first trade agreement to include patent system harmonization obligations. Mexico as a signatory to NAFTA was the first developing country to adopt the patent system of developed countries via patent system harmonization. This makes Mexico a particularly relevant case study on the subject. The central research question addressed in this dissertation is: Does NAFTA patent system harmonization promote access to medicines in Mexico, while incentivizing pharmaceutical R&D? This dissertation undertakes a comparative legal analysis, a scoping study, and qualitative stakeholder analysis to address the central research question. Evidence is provided that compulsory licensing as a safeguard is inadequate as a downstream measure in the promotion of access. A key finding is that international trade agreements should be drafted with optimal pharmaceutical patent protection standards in mind. Further, patent system harmonization results in a net health benefit that can be maximized through the provision of feedback evidence to decision-makers in order to develop responsive laws and policy. This dissertation proposes that: if we reform the granting of patent terms from a fixed twenty year life period to a flexible and adjustable term determined through an assessment of health and economic conditions that exist during any given time period, we will improve both global equity in access to medicines and reduce economic inefficiencies in our current model for pharmaceutical R&D, while maintaining adequate incentives to conduct pharmaceutical R&D. The proposed reform is akin to the use of interest rates as an economic growth and stabilization tool in monetary policy. It would require government patent offices to analyze global conditions in pharmaceutical access and R&D, and accordingly adjust the number of years of patent protection awarded. This novel contribution to the academic literature informs Canadian, Mexican, and developing country decision makers on how to design appropriate policy for the benefit of public health.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".