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Record W2314952115 · doi:10.5509/2006793483

Vicarious Traumas: Television and Public Opinion in Japan's North Korea Policy

2006· article· en· W2314952115 on OpenAlexaffvenue
Hyung Gu Lynn

Bibliographic record

VenuePacific Affairs · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPublic opinionPolitical scienceMedia studiesAdvertisingHistorySociologyBusinessLawPolitics

Abstract

fetched live from OpenAlex

Critiques often dubious cheerleading of the US war against Iraq have become familiar elements of recent public discourse. However, there have not been many similar analyses of media representations of North Korea. Considering how such representations can shape perceptions of North Korea among the public, academics and policy makers, and how difficult it has been to obtain accurate information on North Korea, this relative paucity is surprising. I address this lacuna by analyzing the role of the Japanese media, particularly television, in generating public perceptions of North Korea. While I focus on Japanese television, several of the implications of the analysis are applicable to media and public opinion in other countries. The caveat is that media coverage of North Korea in Japan has maintained near-saturation levels for the past four to five years, particularly since the build-up to the first Kim Jong U/Koizumi Junichiro Pyongyang summit on September 17, 2002 (hereafter 9/17), at which Kim first acknowledged that North Korea had abducted Japanese nationals in the past. In relative terms, media coverage of North Korea in countries other than Japan and South Korea has been sporadic and thin, in both quantity and quality. There are several works in English on the Japanese media; however, these have often converged around the role of the media in domestic politics or USJapan relations.2 Additionally, the North Korea-Japan binary has often

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.268
Teacher spread0.248 · 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 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

Citations16
Published2006
Admission routes2
Has abstractyes

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