NAFTA 20 Years Later
Bibliographic record
Abstract
Enactment of the North American Free Trade Agreement (NAFTA) among the United States, Mexico, and Canada 20 years ago advanced economic integration and started a public debate running to today about the merits of trade agreements in the era of globalization. As the first major trade accord between two wealthy countries and a relatively poor country, NAFTA created enormous opportunities in all three economies while generating anxieties about job losses and other kinds of displacement. Mexico and the United States have clearly reaped great gains at the aggregate level from their more open trading and investment relationship, but NAFTA is frequently invoked as a job-killing precedent by opponents of further US trade agreements with poorer countries. On July 15, 2014, the Peterson Institute for International Economics convened a conference, "Mexico and the United States: Building on the Benefits of NAFTA," to assess both benefits and costs derived from this important trade accord. In addition, the Institute's president, Adam S. Posen, has summarized his view of the impact in an op-ed essay. This report, part of a new series of publications called PIIE Briefings, collects recent writings by PIIE scholars on NAFTA, including some previously published papers and the transcript of the NAFTA conference. The Institute is proud that these papers and presentations are in keeping with our customary intellectual rigor, objectivity, and research-based conclusions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.028 |
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".