A Critical Discourse Analysis of Intellectual Property Rights Within NAFTA 1.0: Implications for NAFTA 2.0 and for Democratic (Health) Governance in Canada
Bibliographic record
Abstract
In 1993, the Canadian federal government ratified the North American Free Trade Agreement (NAFTA). Prior to ratification, compulsory licensing was eliminated from Canada's Patent Act and intellectual property rights (IPRs) were strengthened. Compulsory licensing allows competitors to produce drugs under patent without the consent of the patent holder, challenging drug monopolies and lowering prices, whereas IPRs lengthen patent protections, shielding patent holders from competition and increasing prices. We perform a critical discourse analysis of key provisions in Chapter 17 of NAFTA in light of industry claims that pharmaceutical innovation requires important investments in research and development, justifying high drug prices. We note that since NAFTA, spending in research and development in Canada has decreased and drug prices have increased, becoming a major barrier to equitable access to critically necessary medications. We argue that by modifying the law, the federal government has wronged the Canadian people by discursively appropriating the language of protecting the public good while in practice legitimizing and consolidating private drug development and production, legalizing exorbitant profits, and excluding well-tested publicly financed alternatives. While NAFTA has now been superseded by the Canada-United States-Mexico Agreement, our analysis offers important lessons moving forward.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.037 | 0.057 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".