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Record W2341720704 · doi:10.1177/1742766515626827

BRICS summit diplomacy: Constructing national identities through Russian and Chinese media coverage of the fifth BRICS summit in Durban, South Africa

2016· article· en· W2341720704 on OpenAlexaff
Natalia Grincheva, Jiayi Lu

Bibliographic record

VenueGlobal Media and Communication · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsConcordia University
Fundersnot available
KeywordsSummitChinaPolitical scienceDiplomacyPoliticsPublic diplomacyGeographyLaw

Abstract

fetched live from OpenAlex

This study identifies, analyses and compares media content produced by Russian and Chinese TV channels surrounding the events of the fifth BRICS summit in Durban, South Africa, in 2013. The study utilizes a comparative frame analysis to deconstruct and explain media messages communicated by Russian and Chinese media representing national identities of the countries through the BRICS summit diplomacy. The study discusses important questions with regard to the cultural, political and economic contexts that shape the perceptions of the roles and ambitions of Russia and China on the world stage. The major findings clearly demonstrate that Russian and Chinese media adopted different rhetorical frames to portray their national identities through the media coverage of the fifth BRICS summit. These positions imply an interior (in the case of China) or a straightforward (in the case of Russia) approach to communicate a form of ‘collective resistance’ to the global arena, where the countries seek larger global recognition and appreciation.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
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 head0.026
GPT teacher head0.287
Teacher spread0.261 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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