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Record W3205149406 · doi:10.1080/02560046.2021.1985550

North Korean Defectors’ Self-representation as the Politics of Refusal

2021· article· en· W3205149406 on OpenAlexaff
Kyong Yoon

Bibliographic record

VenueCritical Arts · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsRepresentation (politics)MainstreamNegotiationPoliticsContext (archaeology)Media studiesBroadcasting (networking)SociologyPolitical scienceGender studiesHistoryLawSocial scienceComputer scienceComputer security

Abstract

fetched live from OpenAlex

While the number of North Korean defectors in South Korea has increased, their portrayal in the mainstream media remains marginal and biased. Given this context, this article explores how North Korean defectors have gradually engaged in self-representation through new media technologies, such as personal broadcasting on digital platforms. In so doing, it reveals that the defectors at least partially challenge the dominant representation or lack of representation of North Koreans and seek self-representation. Drawing on scholarly literature and textual analysis of North Korean defectors’ self-representation through Internet personal broadcasting, this study examines how defectors explore different ways of negotiating the dominant construction of themselves as the other of South Koreans.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.044
GPT teacher head0.351
Teacher spread0.307 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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