GIANNINI FOUNDATION CONFERENCE North American Agriculture: Assessing NAFTA at 12
Bibliographic record
Abstract
Thanks to its controversial creation, NAFTA has served as the American touchstone for subsequent trade agreements. Those who championed NAFTA generally applaud the FTAs negotiated since 1994; those who fought NAFTA usually view FTAs with skepticism. Congressional debate over the CAFTA-DR pact was heavily colored by NAFTA memories, and the same fate seems sure to overtake the FTAA, if a Hemispheric agreement is ever negotiated. This essay, extracted from our recent volume NAFTA Revisited: Achievements and Challenges, summarizes some assessments that Jeffrey J. Schott and I make of the NAFTA experience. In June 1990, Mexican President Carlos Salinas de Gortari and President George H.W. Bush announced a daring initiative: the creation of a free trade area between the United States and Mexico. When formal negotiations began one year later, Canada—spurred on by fears that its benefits from the 1989 Canada-US Free Trade Agreement (CUSFTA) might be diluted—had joined in the project. Negotiations on the North American Free Trade Agreement (NAFTA) proceeded to create one of the world’s largest free trade blocs.1 Upon entering into force in January 1994, NAFTA represented a $6 trillion
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".