Bibliographic record
Abstract
The renegotiation of NAFTA was surrounded by a dramatic atmosphere, just as Canadian Minister of Foreign Affairs Chrystia Freeland predicted. The negotiations took place against a backdrop of unilateral trade measures, President Trump’s mercantilist approach to trade policy, and the United States’ specified preference for bilateral trade deals. This article argues that, for the most part, economic, political and cultural relations in the NAFTA countries are bilateral in nature, but with important trilateral production chains in specific sectors, most notably in the automotive sector. Beyond these trilateral sectors, the relationship between Canada and Mexico plays a relatively minor role. However, replacing NAFTA with bilateral agreements would have placed Canada and Mexico at a disadvantage, relative to the United States, in terms of attracting foreign direct investment. Nevertheless, Canadian and Mexican interests do not always coincide, nor do their negotiating positions. For example, Mexico was willing to give up Chapter 19 dispute settlement for trade remedies, whereas Canada insisted on keeping it in place. In end, USMCA Chapter 10 preserves this dispute settlement mechanism for all three parties. Canada was willing to give up NAFTA Chapter 11 on foreign investment disputes, whereas Mexico accepted a modified version. The result is a trilateral agreement with significant bilateral elements, as well as global elements that will serve as a possible model in future megaregional and multilateral negotiations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".