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Record W2521726190 · doi:10.17742/image.scandal.4-1.12

CONTEMPORARY ARAB MUSIC VIDEO CLIPS: BETWEEN SIMULATING MTV’S GENDER STEREOTYPES AND FOSTERING NEW ONES

2013· article· en· W2521726190 on OpenAlexvenueno aff
Ouidyane Elouardaoui

Bibliographic record

VenueImaginations Journal of Cross-Cultural Image Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPopular musicMusicalNarrativeStyle (visual arts)Human sexualitySociologyArtAestheticsLiteratureMedia studiesGender studies

Abstract

fetched live from OpenAlex

MTV was the first 24-hour cable network to continuously broadcast music videos in the 1980s, thus establishing the genre. Generally, Arab music video clips differ from Western-produced music videos, because artists are compelled to abide by the conservative cultural norms of Arab societies. However, most Arab music videos, particularly those produced in the last decade, reaffirm Western gender stereotypes intrinsic to those produced by MTV. Contemporary Arab music videos for the most part simulate an MTV aesthetic vocabulary of a gaze that features female sexuality and narratives of seduction. Some new Arab producers, nonetheless, have attempted to avoid reproducing MTV-based gender clichés, but have instead created new gender misconceptions. These productions emphasize depictions of women as untrustworthy and impetuous. Finally, I discuss how young Arab singers have constantly been confronted by the ire of regionally prominent religious figures who denounce the music videos’ endorsement of “anti-Islamic” principles. I contend that Stuart Hall and Paddy Whannel’s early analysis of reactions against popular music styles among British youth as a form of cultural transgression remains relevant to understanding the cultural style of Arab youth as expressed through popular musical performance.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0020.012
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 head0.183
GPT teacher head0.434
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

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