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Record W2080846819 · doi:10.1177/1464884907083118

The paradox of journalistic representation of the other

2007· article· en· W2080846819 on OpenAlexaboutno aff
Christine Chi Mei Leung, Yu Huang

Bibliographic record

VenueJournalism · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperGlobeChinaNews mediaRepresentation (politics)Political scienceContent analysisPrint mediaMedia studiesMedia coverageAdvertisingGeographyHistorySociologySocial scienceBusinessLawPoliticsPsychology

Abstract

fetched live from OpenAlex

This paper primarily looks at one of the essential aspects of global (usually western) journalists' praxis of covering and depicting the other (generally the non-western). Content analysis of quantitative and qualitative attributes of media coverage of the SARS outbreak with regard to China and Vietnam from newspapers in five countries, including the Washington Post (USA), The Times (UK), the Sydney Morning Herald (Australia), the Globe & Mail (Canada), the Straits Times (Singapore), Newsweek and online news was undertaken. Findings show that while the western news coverage on China corroborated the image of the other in an unfavorable light, Vietnam was not portrayed as the negative other. Differences in China's and Vietnam's handling of SARS have affected their news coverage by the media. Both internal forces and external factors, interplaying and often competing, have contributed to the dynamic process of news coverage and image construction in the international media.

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)0.013
metaresearch head score (Gemma)0.033
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0080.026
Scholarly communication0.0150.009
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.386
Teacher spread0.330 · 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

Citations36
Published2007
Admission routes1
Has abstractyes

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