Bibliographic record
Abstract
The Mexican economy has shown a sustained and expanding dynamic of trade liberalization in recent decades. Due to its relevance within the global economic system, Mexico ranks among the countries with the highest export and import volumes, concentrating a significant portion of its international trade with the 21 economies that comprise the Asia-Pacific Economic Cooperation (APEC) mechanism. This liberalization has led to Mexico's integration into regional value chains and fostered manufactured exports. Among these, the United States, the People's Republic of China, Canada, the Republic of Korea, and Japan stand out. Trade with these countries has experienced constant growth since the last decade of the 20th century. However, it is also observed that market liberalization alone does not guarantee technological development or greater equity in the distribution of trade, which for decades has been concentrated with the United States. The objective of this research is to analyze the evolution and participation of Mexico's foreign trade with the APEC economies, and in particular with the five nations mentioned, during the period between 1989 and 2023. The methodology is based on a documentary analysis that allowed the collection and systematization of information from various official and academic sources, in order to explain Mexico's insertion and behavior within the framework of APEC regional trade. as their effects on expectations, return intentions, and social practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".