El transporte internacional como factor de competitividad en el comercio exterior
Bibliographic record
Abstract
Actualmente casi todos los envíos internacionales necesitan emplear más de un tipo de transporte desde su punto de origen hasta su destino final. Cada uno de los tipos mundiales de transporte de carga y pasajeros ha desempeñado un papel esencial en la facilitación de la diversificación geográfica del comercio. En este trabajo se evalúa, a través del método de Análisis de Componentes Principales, la competitividad del transporte internacional considerando, a su vez, las variables más importantes que inciden en este sector y el desempeño de las economías respecto de dichas variables. Se analiza la estructura del sistema de transporte internacional para veintinueve países, entre ellos siete de América Latina. Los resultados mostrados en el índice de competitividad del transporte internacional señalan que los países más competitivos en materia de transporte internacional son, en orden descendente, Hong Kong, Estados Unidos, Singapur, China, Suecia, España, Japón, Bélgica, Dinamarca y Canadá. El estudio destaca también que los países de América Latina con mayores puntuaciones en este rubro son Brasil, seguido de Panamá, Chile y Costa Rica. Currently most of international shipments need to use more than one type of transportation from its point of origin to final destination. Each one of the types of global transport has played an essential role in facilitating geographic diversification. 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, analyzing the structure of the international transport system of twenty-nine countries, including seven Latin American countries. The results shown in the competitiveness index of international transportation indicate that the most competitive countries in this field are: USA, Hong Kong, Singapore, China, Sweden, Spain, Japan, Belgium, Denmark and Canada. Highlighting that, the countries of Latin America with the highest scores in this category are Brazil, followed by Panama, Chile 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".