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Record W4281781508 · doi:10.5430/wjel.v12n5p250

The Representation of the Car as a Social Space in Laila Halaby’s Once in a Promised Land

2022· article· en· W4281781508 on OpenAlexvenueno aff
Hanan Qaoud, Yousef Abu Amrieh

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRussian Literature and Bakhtin Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChronotopeRealmSociologyConformityRepresentation (politics)NarrativeSpace (punctuation)AestheticsPlot (graphics)NationalismEpistemologySocial psychologyPsychologyLawLinguisticsPhilosophyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

The purpose of this study is to examine the representation of the car as a social space in Laila Halaby’s novel Once in a Promised Land (2007) and explore its interrelationship with issues of literary chronotope, counter discourse, and identity formation. The analysis of this study is interdisciplinary in nature; it tackles the car across social, psychological, and literary domains. Though the article takes Bakhtin’s theory of chronotope as a point of departure; it puts forth Henri Lefebvre’s theory of the production of social space to explicate how Halaby’s characters utilize their cars to incessantly produce social relationships with people from underclasses. In view of these two theories, it is found that Halaby transposes the semiotic function of the car, propelling it from the realm of conformity and nationalism to the realm of resistance and socialism. Halaby’s characters are depicted struggling to unshackle themselves from the stereotyping images imposed on them. By presenting the car as a medium and means for socializing with people from distinct social and ethnic backgrounds, Halaby generates a holistic humanistic narrative that deconstructs the racist and binaries thinking that pervaded the official discourse in post 9/11 America.

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)0.001
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 score0.267
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.294
Teacher spread0.285 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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