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Record W2540510514

Cultural Policy in the Korean Wave: An Analysis of Cultural Diplomacy Embedded in Presidential Speeches

2016· article· en· W2540510514 on OpenAlexaff
Taeyoung Kim, Dal Yong Jin

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFraming (construction)ViewpointsReinterpretationDiplomacyPresidential systemCultural policyPolitical scienceKorean WavePublic diplomacyGovernment (linguistics)SociologyPolitical economyPublic administrationPoliticsLawAestheticsAdvertisingHistoryLinguistics
DOInot available

Abstract

fetched live from OpenAlex

<p>This article examines the changes and developments of the Korean government’s attitude to the Korean Wave, connecting with the notion of cultural diplomacy. It investigates presidential speeches and statements as well as other governmental documents between 1998 and 2014 because they represent and establish guidelines applying to cultural policies. By analyzing presidential statements with the notion of cultural diplomacy, it explores the government’s reinterpretation of this transnational, hybrid cultural content into national products, thereby appropriating them as tools of improving national images. Throughout the research, this article connects presidents’ viewpoints with their subsequent cultural policies, thereby finding fundamental perspectives framing cultural policies vis-à-vis the Korean Wave. </p>

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)0.005
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0000.001
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 head0.280
GPT teacher head0.602
Teacher spread0.322 · 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

Citations37
Published2016
Admission routes1
Has abstractyes

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