China-Mexico Economic Relationship in the Context of China’s Penetration in Latin America
Bibliographic record
Abstract
China’s relationship with Latin America countries experienced a process of significant expansion over the past two decades. Even though Latin America was not considered as part of the BRI when it was first established, China designed its economic strategy toward the region by emphasizing investment, financial and industrial capacity cooperation, besides trade. Afterwards, China invited Latin American countries to participate in the BRI at the China–Community of Latin American and Caribbean States (CELAC) Ministerial Forum in Santiago in January 2018. Twenty-one Latin American and Caribbean (LAC) countries have signed up to the BRI. However, there are few LAC countries that have not done so, among them the region’s largest economies such as Brazil, Mexico and Colombia, although Brazil is with full AIIB membership. This paper focuses on the specific economic relations between China and Mexico, as far as Mexico is the less involved LA country into the BRI, and Mexico’s position toward China is more aligned with that of its North American partners than to the Latin American countries. This article analyzes the specific situation of Mexico, characterized by its deep integration and dependency on the US market and examines the current economic relations of China and Mexico, considering the impact of the United States–Mexico–Canada Agreement on the bilateral economic relations. The structure of bilateral trade between Mexico and China is also examined, using the Grubel-Lloyd Index (GLI).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".