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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.997

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 studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.087
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