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Record W4413908744 · doi:10.5430/wjel.v16n1p305

The Politics of Representing Gender and Sexuality in Arundhati Roy’s Selected Books

2025· article· en· W4413908744 on OpenAlexvenueno aff
R. Ranga

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsHuman sexualityGender studiesSociologyReligious studiesPolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

Arundhati Roy, through her powerful blend of fiction and non-fiction, shatters conventional representations of gender and sexuality in contemporary India. This paper delves into Roy's work, specifically “Broken Republic” (non-fiction essays) and “The Ministry of Utmost Happiness” (novel), to explore how she dismantles societal expectations across different social backgrounds. “Broken Republic” serves as a potent critique, where Roy exposes the patriarchal structures and dominant narratives that confine and define gender and sexuality. The contrasting titles, “Khwabgah” (dream world) and “Kondh” (an indigenous tribe), hint at the spectrum Roy explores. Through this comparison, we can analyse how Roy portrays the experiences of marginalised communities like women, LGBTQ+ individuals, and religious minorities, who face various forms of discrimination that intersect with their gender and sexual identities. This research paper examines how Roy employs both fiction and non-fiction to champion agency and resistance. Ultimately, the paper also explores how Roy uses her voice to amplify those silenced by societal norms, advocating for social change and a more inclusive vision of both gender and sexuality in contemporary India. The paper uses intersectionality theory to examine and analyse the two texts to bring out the nuances in how Roy has portrayed women and transgender individuals and the power dynamics that exist in their respective worlds.

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)0.001
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 score0.326
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.252
Teacher spread0.238 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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Same venueWorld Journal of English LanguageSame topicSouth Asian Cinema and CultureFrench-language works237,207