Navigating U.S.-China Trade War: Mexico’s Economic Diplomacy in Perspective
Bibliographic record
Abstract
China’s growing involvement in trade and foreign investments across Latin America and the Caribbean (LAC) has impacted regional countries’ relations with the United States, the traditionally dominant power. In the case of Mexico, China is expanding its presence in the Mexican economy, which could either strengthen or complicate the Mexico-U.S. relationship. The paper aims to examine the impact of China’s role in the Mexican economy within the framework of Mexico-U.S. Ties and the U.S.-China trade war. It argues that the trade war augments Mexico’s participation in the U.S. market through the U.S.-Mexico-Canada Agreement (USMCA). The empirical analysis demonstrates that Mexico has leveraged the U.S.-China trade war under the new rules established by the USMCA. The new rules of origin and labor provisions give Mexico a greater advantage in accessing the U.S. market and enhance the country’s appeal to foreign companies for nearshoring, despite the asymmetrical nature of Mexico-U.S. relations. Meanwhile, rather than hindering Mexico-China trade, the prohibition stipulated in the USMCA against signing a trade agreement with a non-market economy has helped expand trade links between the two countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".