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Record W4385876231 · doi:10.5430/wjel.v13n7p414

Male Objectification in Jokha Alharthi’s Celestial Bodies: A Deconstructive Reading

2023· article· en· W4385876231 on OpenAlexvenueno aff
Omaima K. AlTobi, Met’eb Ali Alnwairan

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsnot available
FundersDivision of Graduate Education
KeywordsObjectificationConversationReading (process)OppressionFeminismSociologyIntersectionalityGender studiesEpistemologyPsychologySocial psychologyLinguisticsPolitical sciencePoliticsPhilosophy

Abstract

fetched live from OpenAlex

This paper explores the often-overlooked issue of male objectification in literature, using Jokha Alharthi’s novel Celestial Bodies (2019) to illustrate the concept. While traditional objectification theory focuses primarily on the objectification of women, this study seeks to broaden the conversation by analyzing how male objectification is depicted in the novel. This paper utilizes a deconstructive reading to examine how the novel depicts both men and women fighting against objectification and oppression. In addition, the study examines the evolution of Feminism in traditional Arabic society and sheds light on the role of literature in challenging societal norms and advancing critical thinking. By uncovering the multiple meanings and perspectives within the text, this study contributes to a broader discussion of objectification in literature and society. This paper suggests that Jokha Alharthi’s Celestial Bodies is an important contribution to feminist discourse and an essential read for those interested in understanding the intersectionality of gender and objectification in literature.

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)0.004
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.026
Scholarly communication0.0070.007
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.310
Teacher spread0.293 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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