Infringing on Investment: How One Company is Using Investment Protections of NAFTA to Save its Intellectual Property
Bibliographic record
Abstract
In 1994, twenty years ago, the United States, Canada, and Mexico signed the North American Free Trade Agreement ("NAFTA") into law. 1 It had a number of goals, including the intent "to eliminate barriers of trade and investment between the United States, Canada and Mexico." 2 NAFTA has a number of provisions to achieve its goals, including decreasing the tariff between the three countries in order to increase trade.3 The decreasing tariff was intended to lower trade barriers with the hope of making consumer products cheaper.4 With respect to a number of consumer products, this hope was made into a reality.5 Importation from Mexican and Canadian factories without trade barriers made a number of goods, including automobiles, electronics, and clothing, cheaper in the United States.6 However, there is one area of consumer products that did not achieve the hoped for price decline in the United States: the prescription drug industry.There is a long-held belief in the United States that prescription drugs are made available at cheaper prices in countries such as Canada because of the availability of generic drugs.7 While this may not always be true in the cases of some prescription drugs, 8 there is a basis for this claim.9
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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.028 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 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".