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Record W2610563744 · doi:10.1386/macp.13.1-2.39_1

Media and sectarianism in the Middle East: Saudi hegemony over Pan-Arab media

2017· article· en· W2610563744 on OpenAlexaff
Mohammad Yaghi

Bibliographic record

VenueInternational Journal of Media and Cultural Politics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsQueen's University
Fundersnot available
KeywordsSectarianismHegemonyPoliticsPolitical scienceTerrorismCoercion (linguistics)Political economyPower (physics)Order (exchange)Media studiesLawSociologyBusiness

Abstract

fetched live from OpenAlex

Abstract Powerful political and economic players allocate significance resources in order to influence the media in ways that advance their interests. This article attempts to reveal the mechanisms that the ruling Saudi family uses to exert its power over pan-Arab media. It argues that Al Saud uses ownership, sponsorship, cooptation and coercion to keep pan-Arab media in line with its policies. It also asserts that the main consequence of Saudi domination of pan-Arab media today is not to divert Arab public opinion away from politics, as was the case in the 1990s; rather it is to set a sectarian agenda as a means to counter Iran’s influence in some Arab countries. Using frame analysis of a representative sample of eight programmes broadcast between 2012 and 2016 by three Salafi TV channels owned and sponsored by Al Saud, the article demonstrates that sponsoring a sectarian agenda may also encourage terrorism. It also reveals frame convergence between the messages of the Salafi channels and that of ISIL. Saudi media policy thus increases the visibility of ISIL’s messages and renders them credible, leading to the expansion of ISIL’s capability for recruitment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.062
GPT teacher head0.317
Teacher spread0.255 · 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 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

Citations7
Published2017
Admission routes1
Has abstractyes

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