NAFTA Renegotiation: US Offensive and Defensive Interests vis-à-vis Canada
Bibliographic record
Abstract
Previous US administrations—whether Republican or Democrat—have focused on reducing barriers to trade and investment during trade negotiations, but the Trump administration will prioritize reducing the US trade deficit when it renegotiates the North American Free Trade Agreement (NAFTA). Trump will seek to lower Canadian barriers to US exports and oppose changes that would lower US barriers to Canadian exports. The authors identify well-known US and Canadian trade barriers and speculate on possible “blockbuster” demands that the Trump trade team might make on Canada in keeping with Trump’s concept of unfair trade (e.g., border tax adjustment, rules of origin, and currency undervaluation). NAFTA renegotiation gives the Trump administration an opportunity to resolve longstanding trade grievances with Canada, provided the United States makes its own concessions. Both countries can benefit from updating NAFTA to address issues not foreseen in the early 1990s, such as digital commerce and state-owned enterprises. But US insistence on “blockbuster” demands could put not only the talks but also the entire relationship between Ottawa and Washington at risk.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".