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Record W4386252273 · doi:10.5430/wjel.v13n7p549

The Depiction of Violence as a Social Reality: A Cross Sectional Analysis of Mahasweta Devi’s The Hunt and Indira Goswami’s The Beasts

2023· article· en· W4386252273 on OpenAlexvenueno aff
Saraswathy Selvarajan, R. Preetha

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsPatriarchyCrueltySubalternGender studiesSociologyCasteSexual violencePoliticsCriminologyLawPolitical science

Abstract

fetched live from OpenAlex

The paper attempts to explore the violence meted out to gendered subaltern and also scrutinizes the destructive role of patriarchy in the lives of women protagonists in the writings of Mahasweta Devi and Indira Goswami. Violence against women is as old as the world. The kind and the intensity of violence vary from time to time and place to place, but it is there everywhere. Mahasweta Devi and Indira Goswami are some of the rare women writer-activists who have taken a daring attempt to expose the kind of violence faced by the women in their everyday life. Their writings expose the cruel deeds of patriarchy. The cruelty happening in the lives of gendered subalterns in terms of caste and class is heart breaking. Through their writings, they advocate them that protest is the only choice to stop such atrocities against women instead of being a mute observer. Only by resisting, women can minimise the violence committed on them. Women protagonists of Devi and Goswami are trying to protest and they challenge patriarchy. The gender based violence like rape and sexual assault threatens the well-being and the dignity of women. The paper analyses Devi’s short story The Hunt (2001) and Goswami’s The Beasts (2002) in bringing about the stark reality and cruelty occurred in the lives of “gendered subaltern” of the contemporary West Bengal and Assam in the 90’s through their writings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.022
GPT teacher head0.337
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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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