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Record W2954935646 · doi:10.5539/ijel.v9n4p192

A Critical Discourse Analysis of ‘Fire and Fury: Inside the Trump White House’ by Michael Wolff

2019· article· en· W2954935646 on OpenAlexvenueno aff
Fareeha Aazam, Fatima Zafar Baig, Tanveer Baig, Shumaila Khaliq, Amna Azam, Sarah Shamshad, Muhammad Zammad Aslam

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyWhite (mutation)SociologyCritical discourse analysisIdentity (music)Discourse analysisValue (mathematics)Social practicePoliticsGender studiesAestheticsLawLinguisticsArtArt historyPolitical sciencePerformance artPhilosophyComputer science

Abstract

fetched live from OpenAlex

Language plays a pivotal role in constructing identity and ideology. It shapes the people’s ideas and beliefs about specific perspectives. This research is mainly concerned with constructed ideologies through discourse. The present study is based on the book “Fire and Fury: Inside the Trump White House” written by Michael Wolff. The approach for present research is qualitative in nature and for analysis; different extracts are taken from the book. It adopts Fairclough’s (1992) three-dimensional model for analysis which includes textual, discursive practice and social practice analyses. The study reveals that a constructed ideology of Trump is portrayed in this book, in which, he is presented as ineligible and unfit person for the post of president of United States. Thus, in conclusion, the value of discourse in conveying the specific ideology cannot be underestimated.

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)0.006
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 score0.013
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0110.016
Scholarly communication0.0060.007
Open science0.0010.003
Research integrity0.0020.003
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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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