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Record W4385875644 · doi:10.5539/ells.v13n3p29

Mind of Darkness: Social Equality and Self-Autonomy as Feminist Premises of the Concept of Courageous Code in Yaa Gyasi’s Homegoing

2023· article· en· W4385875644 on OpenAlexvenueno aff
Abdalhadi Nimer Abdalqader Abu Jweid

Bibliographic record

VenueEnglish Language and Literature Studies · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicIndian History and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsOppressionAutonomySociologyGender studiesNarrativeSubjectivityCode (set theory)Style (visual arts)AestheticsArtLawLiteraturePolitical sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

Yaa Gyasi’s Homegoing presents the horrific sequences of black women’s experience throughout history. Such experience encompasses the plights—both mundane and spectacular—of women’s marginalization and deprivation. Gyasi’s narrative style, by turns historical and racially intimate, evokes common themes of misogynoir; and her novel abounds with deprived protagonists and androcentric entities. Focusing on black women’s experience, this study theoretically attempts to explore the concept of feminist “courageous code” as an antithesis of misogynoir to empower their social equality and self-autonomy. The study critically considers how Gyasi utilizes a historical aesthetic narrative in her writing to critique and unravel the unspeakable oppression experienced by black women. Interpreting the intersection of literary oppression and theoretical “courageous code,” this study argues that Gyasi’s Homegoing bridges the gap between the oppressed black women and patriarchal stereotyping, reinforcing the fictional expectations of her largely gender equality. The study, therefore, seeks to find authenticity, look for female subjectivity and self-autonomy; and it anticipates abjection of misogynoir and all its implications via feminist “courageous code.”

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.262
Teacher spread0.244 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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