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Record W4362698632 · doi:10.5430/wjel.v13n5p261

Relocating a Torn Identity and Asserting the Right of the Oppressed to be Heard and Liberated in Mohja Kahf’s The Girl in the Tangerine Scarf

2023· article· en· W4362698632 on OpenAlexvenueno aff
Md Abu Shahid Abdullah

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGirlSecularismIdentity (music)AssertionSubjectivityOrder (exchange)IslamGender studiesSociologyHistoryArtLiteratureAestheticsPhilosophyPsychologyComputer science

Abstract

fetched live from OpenAlex

Recently, there has been a rapid increase in the production of literary works written in English by female Arab writers who have brought more appreciation for the Arab women who are often perceived by the Western reader as exotic, eccentric and complex. The article deals with the 2006 novel The Girl in the Tangerine Scarf by Mohja Kahf, a Syrian-American poet and novelist, which presents the reader with a new borderland area occupied by young Arab-American Muslim women. The novel condemns distorted images of Muslims in America by local media and US foreign policy. The article aims to show the way the protagonist Khadra attempts to relocate an identity which has been lost between home and abroad and between a radical Islam and discerning secularism. Kahf offers the Western reader a unique portrayal of Muslim women, having developed a subjectivity of their own. The article also aims to show that by disclosing the journey—searching for the identity—of Khadra, Kahf enables her to come to terms with both her Arab and American identities where she creates a new identity for herself in order to be accepted and established in America. The new attitude of Muslim-American women resonates with the author’s assertion of the right of the oppressed to be heard and liberated.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.014
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0140.015
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.003
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 head0.025
GPT teacher head0.306
Teacher spread0.281 · 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
Published2023
Admission routes1
Has abstractyes

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