MétaCan
Menu
Back to cohort
Record W3021971038 · doi:10.1177/1750481320917576

Understanding the ideological construction of the Gulf crisis in Arab media discourse: A critical discourse analytic study of the headlines of Al Arabiya English and Al Jazeera English

2020· article· en· W3021971038 on OpenAlexaff
Mohamed Kharbach

Bibliographic record

VenueDiscourse & Communication · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsIdeologyCritical discourse analysisSociologyDiscourse analysisPoliticsConstruct (python library)LinguisticsEpistemologyMedia studiesPolitical scienceLawPhilosophyComputer science

Abstract

fetched live from OpenAlex

This article investigates the ideologisation of Arab media discourse and takes as a case in point the ideological construction of the Gulf crisis in the headlines of Al Arabiya English and Al Jazeera English. A corpus of 515 headlines produced between May and June 2017 is examined using an interdisciplinary critical discourse analytic framework. Analysis is conducted at two levels: a textual level concerned with the analysis of the semantic and syntactic aspects of headlines and a socio-cognitive level informed by insights from Van Dijk’s ideological square concept and his mental model theory and Laclau and Mouffe’s discourse theory. Findings indicate that both platforms are ideologically biased toward the political perspectives of their host states, although in a lesser degree in Al Jazeera English, and also reveal the various discursive strategies used to construct subjective mental models and reference frames to guide readers understanding of the crisis.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0060.011
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.331
Teacher spread0.244 · 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 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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDiscourse & CommunicationSame topicDiscourse Analysis in Language StudiesFrench-language works237,207