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Record W2142777656 · doi:10.5539/ijel.v2n1p59

Post-September 11 Discourse: The Case of Iran in The New York Times

2012· article· en· W2142777656 on OpenAlexvenueno aff
Maryam Jahedi, Faiz Sathi Abdullah

Bibliographic record

VenueInternational Journal of English Linguistics · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsTerrorismCritical discourse analysisRepresentation (politics)Power (physics)Boko haramFront (military)Discourse analysisMedia studiesPolitical scienceNews mediaSociologyPoliticsLinguisticsLawGeographyPhilosophy

Abstract

fetched live from OpenAlex

This study examined how discursive strategies and related linguistic devices were employed by The New York Times (TNYT) to portray Iran after the terrorist attacks in the U.S. on September 11, 2001, and how the media representation may have contributed to negative and/or positive outcomes in terms of geopolitical relations. The study also investigated how sociopolitical assumptions were manifest in producing news about Iran and how the news discourse continued to shape the power relations between the nation and the U.S. in particular, and the world at large. Using Critical Discourse Analysis (CDA) as a multidisciplinary approach, the analysis focused on 171 front-page TNYT news articles from 2001 until 2009. Analysis of the discursive strategies and linguistic means revealed that the news media depicted an overall negative picture of Iran after the September 11 or “9/11” attacks. The effect of this rather stereotypical construction of Iran in TNYT was that of the negative Other, a nation of people that formed part of George W. Bush’s contentious “axis of evil” thesis–malevolent, untrustworthy, violent, and a threat to world peace.

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
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)0.0010.000

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 head0.031
GPT teacher head0.314
Teacher spread0.284 · 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 designNot applicable
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

Citations4
Published2012
Admission routes1
Has abstractyes

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