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Record W3036809908 · doi:10.31703/grr.2019(iv-ii).33

Demystifying the Myth of Genderlect through Intertextuality in Global Media Discourse

2019· article· en· W3036809908 on OpenAlexaff
Aisha Rauf, Shumaila Mazhar, Shabana Akhtar

Bibliographic record

VenueGlobal Regional Review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsTaylor College and Seminary
Fundersnot available
KeywordsIntertextualityCritical discourse analysisIdeologySociologyDiscourse analysisMythologyPerspective (graphical)Power (physics)LinguisticsPoliticsLiteratureComputer sciencePolitical sciencePhilosophyArt

Abstract

fetched live from OpenAlex

This article outlines a gender-based analysis at the connection of critical discourse analysis and intertextuality, with the aim of providing a rich analyses of the multifaceted mechanisms of power and ideology in discourse in supporting hierarchically gendered social orders. The focus of this research is to identify how gender is socially constructed through linguistic choices (especially intertextuality) and discursive practices in media discourse and how these discursive practices in media discourse contribute to create a feminist perspective. The present article has collected 60 talk shows, panel discussions and interviews from 5 global news channels. A corpus is built and a linguistic analysis is conducted where Faircloughs Intertextuality is used as research Methodology. Moreover, Van Dijks socio-cognitive model is used as research framework in which data is analysed on us versus them dichotomy and at micro and macro level of discourse. It is observed that through the use of intertextuality, dominant ideology is (re)- created in feminist media discourse.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.363
Teacher spread0.286 · 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 designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

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