gobierno mexicano y los grupos de autodefensa en Michoacán:
Bibliographic record
Abstract
El presente artículo tiene por objetivo analizar los movimientos de securitización en torno al crimen organizado, sobre todo cuando hay dos actores que lo gestionan simultáneamente; debido a ello, se ha decidido partir del caso mexicano, particularmente en el contexto de la zona de Michoacán, donde los grupos de autodefensa competían –pero al mismo tiempo colaboraban– con el gobierno federal. Ante tal finalidad, el estudio de este proceso se enmarca dentro de un enfoque de securitización que permita contrastar los discursos de ambos actores a través de la instrumentalización de cinco variables: el contexto, los actores securitizadores, el objeto referente, la amenaza, así como la respuesta hacia la misma. Al finalizar, las diferencias entre las concepciones y estrategias de ambos serán útiles para entender las distintas respuestas de la audiencia, la lucha entre imaginarios sociales sobre el crimen organizado y el emprendimiento de determinadas acciones por parte del gobierno federal.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".