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Record W2569572076 · doi:10.18192/rceh.v39i3.1632

Política de una fuga. Memoria y subjetivación política en la representación cinematográfica del poder desaparecedor en Argentina

2015· article· es· W2569572076 on OpenAlexaffvenue
Federico Pous

Bibliographic record

VenueRevista Canadiense de Estudios Hispánicos · 2015
Typearticle
Languagees
FieldPsychology
TopicMemory, violence, and history
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPhilosophyArtPolitical science

Abstract

fetched live from OpenAlex

Este artículo analiza tres películas sobre la representación de los Centros Clandestinos de Detención, Tortura y Exterminio en la última dictadura militar en Argentina (1976-1983): La noche de los lápices (1986) de Héctor Olivera, Garage Olimpo (1999) de Marco Bechis y Crónica de una fuga (2006) de Adrián Caetano. Nuestra lectura toma la idea de la fuga de la película de Caetano con el objetivo de desentrañar cómo estas representaciones cinematográficas, cada una en su momento, desbordaron las narrativas predominantes de la memoria histórica generando una suerte de desidentificación del espectador consigo mismo y con su rol en las políticas de la memoria. Para ello destacamos el análisis de ciertas secuencias específicas o imágenes políticas singulares que produjeron una torsión de la subjetivación y cuestionaron el fundamento político de esas narrativas. Desde esa perspectiva, la figura de la fuga se constituye en un locus de inteligibilidad para pensar cuáles son los límites del proceso contemporáneo de monumentalización de la memoria histórica.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.022
GPT teacher head0.311
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes2
Has abstractyes

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