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Record W2592945567 · doi:10.5539/ells.v7n1p120

Virginia Woolf’s Representation of Women: A Feminist Reading of “The Legacy”

2017· article· en· W2592945567 on OpenAlexvenueno aff
Hussien AlGweirien

Bibliographic record

VenueEnglish Language and Literature Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsGender studiesTheme (computing)WifeDominance (genetics)SociologyPerspective (graphical)Representation (politics)Reading (process)Identity (music)FeminismInequalityAestheticsPolitical scienceLawArtPoliticsVisual arts

Abstract

fetched live from OpenAlex

Over the centuries women have been struggling to gain recognition, calling their independent voice to be heard in patriarchal and racist societies. As they follow the standards and the values of their societies, women tend to break the stereotypical and submissive images that degrade their position in their societies. Thus, this paper will scrutinize thoroughly women’s intellectual ability from a Gynocriticism perspective taking Virginia Woolf’s short story “The Legacy” (published posthously in 1944) as an example. The present paper provides an analytical view of the four models of gynocriticism; i.e., biological, linguistic, cultural, and psychological. It also attempts to shed light on some common feminist themes such as the theme of marriage and how oppressed marriage motivates male dominance. The paper addresses the relationship between wife and husband in terms of gender inequality and women’s identity. It also tackles women’s trapped position as distinct from the liberty of men and oppressed by husband in an unhappy marriage. It relies heavily not only on feminist perspectives as gynocriticism, gender inequality, and the theme of marriage; but also on the authors’ personal life. The paper concludes that being unable to speak their voice freely, women view writing as their salvation for their voice to be heard.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.332
Teacher spread0.315 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2017
Admission routes1
Has abstractyes

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