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Record W2945099598 · doi:10.5539/ells.v9n2p46

A Comparative Study of Competing Discursive Construction of South China Sea Disputes in the Chinese and US English-Language Newspapers

2019· article· en· W2945099598 on OpenAlexvenueno aff
Dan Zhang

Bibliographic record

VenueEnglish Language and Literature Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperChinaHegemonyIdeologyCritical discourse analysisSociologyDiscourse analysisPolitical scienceRepresentation (politics)LawPolitical economyMedia studiesPoliticsLinguistics

Abstract

fetched live from OpenAlex

This study examines the discursive construction of South China Sea dispute in China Daily and The New York Times from April 2016 to December 2017. Drawing on Van Dijk’s account of critical discourse analysis and the linguistic framework of Appraisal theory (Martin & White, 2005), this study investigates how three social actors in the dispute, namely China, United States, Philippines, are differently constructed with the strategic use of attitude resources in the two newspapers. The corpus analyzed consists of 45 newspaper texts from China Daily and 49 newspaper texts from The New York Times. The analysis reveals competing discursive construction of social actors that constitute positive us-representation and negative other-representation in the two newspapers. For example, China Daily constructs China as a peace-loving country, insisting on the peaceful means and the cooperation with ASEAN and other claimant countries to resolve the dispute, whereas The New York Times depicts China as threat, hegemony and provocation. Such competing discursive construction not only reflects the ideological stance of two newspapers, but also functions to legitimize their countries’ policies and decisions in the South China Sea dispute.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.007
GPT teacher head0.260
Teacher spread0.253 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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