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Record W2555342253 · doi:10.5539/ijel.v6n6p118

Journalistic Stance in Newswriting on Iranian Nuclear Issue

2016· article· en· W2555342253 on OpenAlexvenueno aff
Mohammad Hossein Ghane, Fatemeh Mahdavirad

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
FundersInternational Atomic Energy Agency
KeywordsIdeologyPoliticsPresentation (obstetrics)Order (exchange)Critical discourse analysisPolitical scienceSociologyEpistemologyLawPhilosophyEconomicsMedicine

Abstract

fetched live from OpenAlex

Regarding the role of Critical Discourse Analysis (CDA) in discovering the way ideology is crystalized through the prevalence of various discourses, the present study is an attempt to examine how the journalistic personal and institutional ideologies and political positions are realized through certain textual and intertextual features. Using Perrin’s (2012) progression model, journalistic stancing with regard to the Iranian nuclear issue at three levels of micro, meso, and macro was investigated. The study of claims of unpeacefulness in the Western media texts under investigation reveals a systematic ideological bias towards portraying a negative presentation of Iranian nuclear policy. The Iranian journalists, however, tend to highlight the peaceful nature of the Iranian nuclear program and the West’s double standards as well as Iran’s efforts in order to come to a mutual agreement. Implications of the insights provided by the study for confirming the premises of CDA and applications of the findings for teaching are explained in brief.

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)0.005
metaresearch head score (Gemma)0.018
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0040.005
Scholarly communication0.0070.003
Open science0.0000.002
Research integrity0.0010.002
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 head0.020
GPT teacher head0.287
Teacher spread0.267 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicDiscourse Analysis in Language StudiesFrench-language works237,207