Estructura de mercado y comercio intraindustrial en el sector automotriz
Bibliographic record
Abstract
En este documento se analiza la organización industrial que existe en el sector automotriz mexicano y se observa la evolución de las exportaciones en dicha industria, teniendo en consideración la apertura comercial con Estados Unidos y Canadá. Todo esto con la intención de conocer los efectos de las variables como son: la concentración de mercado y la apertura comercial sobre el comercio intraindustrial (cii) en el sector automotriz; a través de teorías que relacionan el comercio internacional con la competencia imperfecta, y que explican con modelos por qué se ha incrementado el comercio intraindustrial en los países industrializados. Entre otros resultados se observa que la estructura de mercado que tiene las ramas del sector automotriz sí tiene un efecto positivo sobre el cii./ In this paper is analyzed the industrial organization that exists in the Mexican automotive industry and immediately observed the development of exports in industry, especially considering the trade liberalization with the United States and Canada. All this with the intention of determining the effects of these variables (market concentration and trade openness) on intraindustry trade (iit) in the automotive sector, through theories linking international trade with imperfect competition and with models that explain why it has increased in industrialized countries the iit. Results show that the market structure in 11the branches of the automotive industry does have a positive effect on the iit.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".