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Record W25889415 · doi:10.1186/s12913-015-0731-5

Estereotipos y cine de género en Kubrick

2010· article· es· W25889415 on OpenAlexfundno aff
José Patricio Pérez Rufí

Bibliographic record

VenueEspéculo: Revista de Estudios Literarios · 2010
Typearticle
Languagees
FieldArts and Humanities
TopicCinema History and Criticism
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsHumanitiesNarrativeArtCharacter (mathematics)ComicsLiteratureMathematics

Abstract

fetched live from OpenAlex

espanolFrecuentemente, la obra de Stanley Kubrick ha sido estudiada desde cuestiones formales o tematicas que rara vez han permitido la entrada a aspectos narrativos. Este articulo analiza la construccion del personaje protagonista desde la concepcion psicologica el mismo, evaluandolo como plano o redondo. Nos atendremos a los referentes previos literarios y narratologicos en el estudio de la dimensionalidad del personaje desde estos criterios. El objetivo sera apreciar el posible uso del personaje como categoria narrativa capaz de otorgar unidad a la obra de un director, convertido asi dicho uso en rasgo estilistico. Veremos que la frecuente activacion de estereotipos en la filmografia de Kubrick procedera del habitual recurso a los generos puros y a los estereotipos asociados a estos, subordinados a objetivos de mayor ambicion intelectual o ideologica EnglishOften the work of Stanley Kubrick has been considered from formal or thematic researches that didn�t almost analyze the narrative creation. This article analyzes the making of the main character as a psychological construction, evaluating his dimensionality as flat or round. We�ll focus on the previous literary and narratologic references in the study of the character�s dimensionality from these criteria. The aim will be to detect the possible use of character as a narrative category able to conform unity in the work of the director, as an stylistic feature. We´ll see that the usual resource to stereotypes in Kubrick's movies will come from the resource to pure genres and to stereotypes associated with these, subordinated to objectives of more intellectual or ideological ambition

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

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 studies0.0030.005
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.010
GPT teacher head0.240
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; 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 designNot applicable
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

Citations2
Published2010
Admission routes1
Has abstractyes

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