MétaCan
Menu
Back to cohort
Record W4389379202 · doi:10.48162/rev.34.068

Rostros afectivos como paisajes en las películas peruanas, Rosa Chumbe (2015) y Magallanes (2015)

2023· article· es· W4389379202 on OpenAlexaff
Marcos Moscoso-Garay

Bibliographic record

VenueCuadernos del CILHA · 2023
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicCultural and Social Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

En este ensayo se analiza el uso de los rostros como paisajes en Rosa Chumbe (2015) y Magallanes (2015) de los cineastas peruanos Jonatan Relayze y Salvador del Solar. Éstos, a lo largo de sus tramas muestran, de manera indirecta y directa, las consecuencias políticas y sociales de décadas pasadas en la sociedad peruana. Sobre todo, las secuelas que trajeron para algunas mujeres, las políticas antisubversivas y el estado de excepción que el gobierno peruano impuso en los años 90 para tratar de controlar los movimientos armados (Sendero Luminoso y el Movimiento Revolucionario Tupac Amaru). Sostengo que estos directores transmiten en sus rostros transformados en paisajes una secuencia de cambios de cualidades o estados afectivos que problematizan la situación de las mujeres víctimas del sistema. En ese sentido, se condensan dos miradas diferentes sobre el pasado, el presente y el futuro de los protagonistas. Así, el paisaje de rostros en primer plano toma centralidad para intentar comprender el entramado de la compleja sociedad peruana de inicios del nuevo siglo. Basándonos en los trabajos sobre paisaje cinematográfico de Martín Lefebvre (2006, 2011), Graeme Harper y Johnathan Rayner (2010, 2013); y el trabajo de Gilles Deleuze (1983) acerca de estudios sobre el cine, analizo las estrategias narrativas y cinematográficas empleadas por estos cineastas para convertir los rostros femeninos en paisajes y, de esta manera, comprender las diferentes miradas de la sociedad peruana.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.004

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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCuadernos del CILHASame topicCultural and Social DynamicsFrench-language works237,207