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Record W2944423705 · doi:10.5539/ijel.v9n3p249

Intertextuality as a Catalyst for Ideology Formation: A Study of Media Discourse Dynamics

2019· article· en· W2944423705 on OpenAlexvenueno aff
Najma Qayyum

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIntertextualitySociologyIdeologyNewspaperCritical discourse analysisDiscourse analysisLinguisticsDynamics (music)Field (mathematics)PoliticsMedia studiesPolitical sciencePhilosophyPedagogy

Abstract

fetched live from OpenAlex

This article investigates how intertextuality in media discourse works as an ideological catalyst. It explores how discursivity and intertextuality in media discourse permeate all levels of society and shape the social and political ideologies of the readers. Media discourse producers are both politicians and reporters. The article investigates how they use language as a manipulating tool. The article also discovers how intertextuality is created in media discourse by clipping specific linguistic elements of different discourses and then forging them together for effect. Four Pakistani English daily newspapers have been analyzed which were selected through non-probability sampling method. The study is qualitative in nature and spreads over six months that is from March 2013 to August 2013. The tenets of critical discourse analysis (CDA) were employed as the main research tool. For understanding the linguistic aspects, Fairclough’s (1995) idea of texts and genres was used and for interpreting the contextual use of language, Halliday’s ideas of field, tenor and mode were incorporated. The analyses revealed how politicians as well as reporters instill tacit ideas into the minds of their addressees which blur and downplay their thinking pattern and entice them to think and behave the way these discourse producers want them to.

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)0.000
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.320
Teacher spread0.299 · 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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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