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El cuento femenino, el cuerpo subversivo en la narrativa hispano canadiense

2013· article· es· W1557484790 on OpenAlexaboutno aff
Luis Molina

Bibliographic record

VenueAmericanae (AECID Library) · 2013
Typearticle
Languagees
FieldArts and Humanities
TopicCultural and Social Studies in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSubversionArtPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Hispanic literature produced in Canada very frequently revolves around the theme of migration and related phenomena, such as political violence, dictatorships, national disenchantment, the Ulysses syndrome, the feeling of being from everywhere and nowhere at once, and the endless quest to belong somewhere in the world. Works written by Hispanic-Canadian women are no exception to this tendency. Furthermore, because of her double condition as a victim in these migration processes and as a woman, the protagonist female subject seems to provide a fuller picture of all the phenomena that the conflicts within a cultural text produce. In this article, I am interested in examining the nature and functioning of subversion as it relates to the way the female figure is portrayed by Hispanic-Canadian women narrators. Specifically, I explore the survival mechanisms used by the characters to overcome adversity. To do so, I use the theory of gender captivity (teoría de los cautiverios de género) to analyze the following five short stories: “El tren” [The train] by Nela Rio, “La oportunidad perfecta” [The perfect opportunity] by Carmen Rodríguez, “El regreso” [The return] by Gabriela Etcheverry, and “La gorda” [The fat woman] by Camila Reitmers.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0460.001

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 head0.218
Teacher spread0.209 · 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
Published2013
Admission routes1
Has abstractyes

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