Perspectivas de la industria mexicana ante la renegociación del tlcan y China
Bibliographic record
Abstract
El documento ofrece un diagnostico sucinto de la polarizacion economica inherente al proceso de reformas de apertura en Mexico y sus multiples instituciones -en particular el Tratado de Libre Comercio de America del Norte (tlcan)- y principales actores (Estados Unidos y China). Posteriormente, se avanza en la comprension de algunos capitulos y articulos del Tratado Mexico-Estados Unidos-Canada (t-mec) que, en caso de ratificarse, reemplazara al tlcan. La revision del t-mec se concentra en la industria autopartes automotriz (iaa), la maxima ganadora de la integracion de America del Norte. Se sostiene que el t-mec sin hacerlo explicito, busca elevar la competitividad de las exportaciones en la iaa de la region (principalmente de Estados Unidos) frente a China. Efectivamente, se desarrollo un analisis de competitividad que permite concluir que los segmentos en la iaa donde Estados Unidos ha perdido competitividad frente a China en la region de America del Norte, son los que presentan las reglas de origen mas rigidas. En la ultima seccion se presenta un grupo de recomendaciones encaminadas a reactivar a la industria mexicana. Las recomendaciones subrayan el papel de la Inversion Extranjera Directa (ied) en la transferencia de conocimiento y tecnologia; la identificacion de industrias con potencial de integracion glocal (v.gr., la metalmecanica); los intersticios que se pueden abrir para la economia mexicana en el escenario de ratificacion del t-mec y diversas medidas minimas a considerar para mejorar la relacion economica de Mexico con China
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".