Bibliographic record
Abstract
Air and water pollution blighted northern Mexican cities long before the North American Free Trade Agreement (NAFTA) was a glimmer on the political horizon. Not surprisingly, when NAFTA became a political reality, environmentalists argued that commercial competition would weaken environmental standards in Canada and the United States and industrial growth in Mexico would further damage its weak environmental infrastructure. NAFTA's huge success in expanding free trade has concentrated population and environmental abuse at the US-Mexico border where it is most visible to Americans. Many environmental groups blame NAFTA and, drawing on its experience, now oppose new trade initiatives.Does the NAFTA record on the environment since 1994 justify its criticism? In this seven-year analysis, the authors review NAFTA's environmental provisions, including a side accord--the North American Agreement on Environmental Cooperation (NAAEC), the situation at the US-Mexican border, and the trends in North American environmental policy. They emphasize that the environmental problems of North America were not the result of NAFTA and the NAAEC was not devised to address all of them. The authors recommend ways to better NAFTA's environmental dimension in all three countries, and improve living conditions where economic growth is greatest--at the US-Mexican border. It makes more sense to tackle the shortcomings than to lament NAFTA and the economic growth it promotes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".