História do Tempo Presente e América Latina: México - uma entrevista com Eugenia Allier-Montaño
Bibliographic record
Abstract
EntrevistadaEugenia Allier-Montaño es investigadora titular del Instituto de Investigaciones Sociales de la Universidad Nacional Autónoma de México y docente en el Colegio de Estudios Latinoamericanos de la Facultad de Filosofía y Letras de la misma universidad. Doctora en Historia por la Ecole des Hautes Etudes en Sciences Sociales (Francia), realizó una Estancia Posdoctoral en el Instituto de Investigaciones Filosóficas de la unam. Es miembro del Sistema Nacional de Investigadores de México. Su último libro, editado con Emilio Crenzel, es Las luchas por la memoria en América Latina. Historia reciente y violencia política, publicado en inglés como The Struggle for Memory in Latin America. Recent History and Political Violence. Es autora de Batallas por la memoria. Los usos políticos del pasado reciente en Uruguay, que ganó la Mención Honorable del concurso “Mejor libro en Historia Reciente y Memoria 2012” de la Sección “Historia Reciente y Memoria” de la Latin American Studies Association (lasa). Entre otras distinciones, ha sido nombrada en la Cátedra de Estudios del México Contemporáneo de la Universidad de Montreal, Canadá. Actualmente dirige el proyecto de investigación “Hacia una historia del presente mexicano: régimen político y movimientos sociales, 1960-2010”. Entrevista concedida em janeiro de 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".