Anti-dumping and Countervailing Duty Law and Practice: The Mexican Experience
Bibliographic record
Abstract
Although Mexican antidumping and countervailing duty laws are similar in important respects to the unfair trade laws of Mexico’s NAFTA partners, the United States and Canada, there are significant procedural and substantive differences. In light of these differences, and given that Mexico is one of the WTO’s most frequent users of unfair trade laws (including at least 25 actions against United States producers in recent years), the subject warrants closer study and analysis by academics and practitioners alike. This article explores the subject of Mexican antidumping and countervailing duty laws in detail by discussing their origins and historical development; comparing them to their US and Canadian counterparts; and guiding the reader through the life-cycle of Mexican antidumping and countervailing duty cases. It also analyzes the various administrative and judicial review options, including NAFTA’s Chapter 19, along with relevant Mexican court, NAFTA binational panel and WTO Dispute Settlement Body jurisprudence. (The article in slightly different form is Chapter 5 in Trade Remedies in North America (Kluwer Law Int’l, 2010).
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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 | 0.003 | 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 | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".