Competitiveness of the Air and the Sea Cargo Transport of Mexico under the International Trade Frame
Bibliographic record
Abstract
International trade trends have changed the cost structure, pricing, logistics, supply chain and hence comparative advantages. Such trends have similarly defined the trade competitiveness of countries becoming more relevant with the level of integration of global transport networks as a driver of international trade. In this paper we evaluate through Principal Component Analysis methodology the international transport competitiveness considering the most important variables that affect this sector and the economic performance of these variables on transportation. We analyze the structure of the international transport system of twenty-nine countries including seven Latin American countries. The results indicate that the most competitive countries in the field of international aviation and maritime transport are, in descending order, the United States, China, Australia, Panama, Germany, Hong Kong, Chile, Singapore, Korea, Argentina, Belgium, Spain, Canada, Japan and the UK. We stress that the Latin American countries with higher scores in this category are Panama, Chile, Argentina, Brazil, Mexico, Peru and Costa Rica.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".