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Record W4237481248 · doi:10.1386/jammr.8.2.83_1

Visible and invisible: An audience study of Muslim and non-Muslim reactions to orientalist representations in I Dream of Jeannie

2015· article· en· W4237481248 on OpenAlexaff
Katherine Bullock

Bibliographic record

VenueJournal of Arab & Muslim Media Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIslamophobiaOrientalismIslamPrejudice (legal term)BlameRepresentation (politics)DreamSociologyMedia studiesGender studiesHistoryPolitical sciencePsychologySocial psychologyLiteratureLawArtPolitics

Abstract

fetched live from OpenAlex

Abstract Most Muslims lay the blame for the perpetuation of societal prejudice against Islam and Muslims at the feet of the media. Media scholars regularly confirm that negative stereotypes prevail in contemporary western media. Yet there are differences between media representation and the effects on actual people’s attitudes. Empirical research is needed to find out if negative media stereotypes of Muslims, Arabs and Islam are actually linked to widespread societal Islamophobia. This article traces audience reactions to a 1960s U.S. sitcom, I Dream of Jeannie, a show replete with ‘orientalist’ representations of Arabs and genies linked to the collection of traditional stories called The Arabian Nights: Tales from a Thousand and One Nights. The main research finding is that while a variety of responses were uncovered, a consistent difference between non-Muslim and Muslim reactions became apparent: only Muslims noticed orientalist stereotyping the show. I conclude that if North American society is to move beyond Islamophobia and forward to building bridges and integrating Muslims without discrimination into wider society, this cannot be done where non-Muslims do not see negative media stereotypes about Muslims.

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)0.009
metaresearch head score (Gemma)0.006
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 score0.060
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.195
GPT teacher head0.484
Teacher spread0.289 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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