RELEVANCIA ESPECIAL DE LAS PROPUESTAS PARA INTEGRAR UN MERCOMÚN DE AMÉRICA DEL NORTE
Bibliographic record
Abstract
ANTE EL INICIO DE NUEVOS GOBIERNOS EN MEXICO Y ESTADOS UNIDOS, PROPUESTAS PARA INTEGRAR UN MERCADO COMUN EN AMERICA DEL NORTE --A SEIS ANOS DE INICIADO EL TRATADO DE LIBRE COMERCIO-- ADQUIEREN ESPECIAL RELEVANCIA, AFIRMO LA DOCTORA OLGA HANSBERG, COORDINADORA DE HUMANIDADES, AL INAUGURAR EL SEMINARIO LAS RELACIONES DE MEXICO CON ESTADOS UNIDOS Y CANADA, UNA MIRADA AL NUEVO MILENIO, QUE ORGANIZO EL CENTRO DE INVESTIGACIONES SOBRE AMERICA DEL NORTE. HIZO UN ANALISIS SISTEMATICO DE COMPLEJAS RELACIONES ENTRE LOS PAISES DE AMERICA DEL NORTE A LA LUZ DEL NUEVO SIGLO. LA REDEFINICION DEL ORDEN POLITICO Y ECONOMICO MUNDIAL ES UN EJEMPLO DE MULTIPLES TAREAS DE INVESTIGACION QUE REALIZA ACTUALMENTE LA UNIVERSIDAD NACIONAL. ARGUMENTO QUE CON ESTE SEMINARIO BUSCA EXAMINARSE DESDE DIVERSOS ENFOQUES LA PROBLEMATICA CONCRETA DE RELACIONES DE MEXICO, CANADA Y ESTADOS UNIDOS PARA PRESENTAR UN PANORAMA CLARO DE LA SITUACION ACTUAL, DEFINIR LOS POSIBLES ESCENARIOS FUTUROS Y PLANEAR ALTERNATIVAS DE SOLUCION. LOS DIAS 23 Y 24 DE AGOSTO SE REALIZO EL SEMINARIO, EN EL AUDITORIO MARIO DE LA CUEVA, DE LA TORRE II DE HUMANIDADES.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".