The Ten Year Track Record of NAFTA: U.S., Mexican and Canadian Farmers and Agriculture
Bibliographic record
Abstract
This fact sheet is part of Public Citizen's "NAFTA at Ten Series" and documents the results of the failed NAFTA model. Before NAFTA, trade agreements dealt with traditional matters such as cutting tariffs and lifting quotas that had set the terms of trade in goods between countries. NAFTA shattered the boundaries of trade agreements; its central focus and most powerful rules concerned investment, and it contained 900 pages of one-size-fits-all "non-trade" rules with significant implications for food safety, drug patents and access to medicines, not to mention jobs, wages and economic security. It also constrained the ability of local government to zone against sprawl or toxic industries. NAFTA was a radical experiment -- never before had a merger of three nations with such different levels of development been attempted. When NAFTA was being debated, proponents and opponents alike predicted its consequences. Now the data are in. What are NAFTA's lessons in Canada, the United States and Mexico? The Free Trade Area of the Americas (FTAA) and Central American Free Trade Agreement (CAFTA) are both proposals to expand NAFTA, but NAFTA's record is playing a significant role in both the hesitance of some FTAA target countries to adopt the NAFTA model and the concerns of U.S. lawmakers to approve CAFTA.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".