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El Hombre / Mujer, el Ángel / Demonio. Cuerpo, misterio e inquietud en Sirena Selena Vestida de Pena

2014· article· es· W192161305 on OpenAlexvenueno aff
Guillermo Severiche

Bibliographic record

VenueEntrehojas Revista de Estudios Hispánicos · 2014
Typearticle
Languagees
FieldSocial Sciences
TopicLatin American Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Resumen: En la novela de Mayra Santos-Febres, la mirada de los demás personajes configura el cuerpo de Sirena Selena como un cuerpo que reconcilia dicotomías, que las fusiona: hombre / mujer, ángel / demonio. Este cuerpo fusionado se idealiza con el fin de despertar el deseo. El mismo sirve como motivación para generar una inquietud tanto en los demás personajes como en los lectores: ¿qué cuerpo es digno de ser amado, de ser deseado? Exploraremos el armado de su cuerpo a través de una subversión de ciertos estereotipos y para ello nos centraremos en dos nociones: la de disidentification y la de tropicalization. El cuerpo funciona aquí como una suerte de entidad que revitaliza estereotipos de género (hombre/mujer), religiosos (angel/demonio) y al mismo tiempo, los deconstruye. El cuerpo se erige como superficie de inscripción y de crítica frente a la artificialidad de estos discursos para mostrar que justamente son artificiales, que son construcciones. Abstract: In Mayra Santos-Febres’ novel, Sirena Selena, the gaze of the other characters configures the protagonist’s body; a body that reconciles dichotomies and merges them: man/woman, angel/demon. This merged body is idolized and its aim is to provoke desire. This body also becomes a motivation to generate an anxiety in the other characters as well as readers: what body deserves to be loved, to be desired? We will explore the assembly of her body through the subversion of certain stereotypes and in order to do that we will focus on two notions: disidentification and tropicalization. The body works as a sort of entity that revitalizes gender stereotypes (man/woman) and at the same time, it deconstructs them. The body becomes a surface of inscription and a form of criticism for the artificiality of these discourses; and it shows their artificiality, their constructiveness.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.290
Teacher spread0.282 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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