Vicisitudes de la competitividad de México frente a socios comerciales: Estados Unidos, China, Canadá y Brasil
Bibliographic record
Abstract
This article aims to carry out a medium-term comparative analysis that allows us to know the evolution and sources of competitiveness of Mexico and its main trading partners: The United States, China, and Canada, which occupy the first, second, and third place, respectively. The analysis includes Brazil as the leading economy in Latin America. Our country benefits from the high trade surplus with partners of the United States-Mexico-Canada Agreement. These benefits contrast with the large deficit with China and other Asian economies. One of the keys to this problem links the country’s competitiveness and industrialization model. In this regard, documentary research is presented according to the systemic-neo-institutional approach that takes up the World Economic Forum of Davos and the World Bank studies. The results reveal a divergence between the WEF’s assessments, which point to a favorable evolution of Mexico’s global competitiveness, and the World Bank, which detects a deterioration in the conditions for doing business with allies to institutional regulatory norms. The analysis ranks Mexico’s competitive advantages and the weaknesses or failures that balk them in the 2011-2019 period.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".