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Record W4389379277 · doi:10.48162/rev.34.067

Paisaje y bagaje histórico en “La cordillera” (2017) de Santiago Mitre

2023· article· es· W4389379277 on OpenAlexaff
Diana Pifano

Bibliographic record

VenueCuadernos del CILHA · 2023
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicCultural and Social Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

En La cordillera (2017) el director Santiago Mitre vuelve a explorar la política a través del personaje de Hernán Blanco, quien, al comienzo de su mandato como presidente de Argentina, se prepara para su primera cumbre de líderes en un hotel en la cima de los Andes. La trama de este thriller alterna entre la vida profesional y personal del presidente ya que, al unísono de las negociaciones de la cumbre, el yerno y la hija de Blanco ocasionan una crisis familiar que amenaza su reputación. Bajo esta presión, tanto las relaciones profesionales como familiares de Blanco comienzan a fracturarse y gradualmente el protagonista se va desligando de colegas y familiares. Ambos aspectos de la vida del presidente confluyen y su ambición desmedida lleva al espectador a cuestionar su carácter moral y preguntarse: ¿hasta dónde será capaz de llegar para beneficiarse a sí mismo? Basándome en el trabajo de Martin Lefebvre (2011), describo cómo la representación cinemática de la cordillera apoya el aislamiento de Blanco. Usando tomas en gran angular e imágenes altamente iluminadas, Mitre (2017) presenta un paisaje deshabitado e inhóspito que acordona y aísla a los personajes. Posteriormente, me apoyo en las ideas de Harper y Rayner (2010) en Cinema and Landscape, para reflexionar sobre el papel de los Andes en la historia de Latinoamérica y como forman parte del bagaje cultural del espectador. A la luz de esta discusión sobre el paisaje, logro ahondar en el comentario político del filme.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.028
GPT teacher head0.258
Teacher spread0.230 · 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

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