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Record W4220762007 · doi:10.5430/wjel.v12n2p55

Reading of Intertextuality in the Notions of Domestic Violence in Select Texts with Reference to Meena Kandasamy’s When I Hit You

2022· article· en· W4220762007 on OpenAlexvenueno aff
J. Sangeetha, S. Mohan, Ahdi Hassan

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsIntertextualityNarrativeReading (process)Domestic violenceContext (archaeology)Exposition (narrative)Variety (cybernetics)LinguisticsSociologyLiteraturePsychologyComputer scienceHistoryPoison controlArtSuicide preventionPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Intertextuality is a term used to describe how humans read and absorb textual information as a result of the development and interaction of texts. The purpose of this research is to present a theoretical exposition of intertextuality. As a result, the current research focuses on domestic violence, a well-documented social problem that impedes women's development in a variety of ways. The study's methodology is based on the intertextualistic narrative of several works, with a special emphasis on Meena Kandasamy's When I Hit You in the context of Domestic Violence. The study's findings reveal that domestic violence has a vulnerable effect on women due to a range of characteristics, and the analysis of many texts in multiple dimensions suggest that, despite the women characters differing in their cultural medium and familial background, they are intimated by their abusive husbands and are subjected to comparable types of violence worldwide.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0090.029
Scholarly communication0.0070.007
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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Same venueWorld Journal of English LanguageSame topicLiterary Theory and Cultural HermeneuticsFrench-language works237,207