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Record W3202086871 · doi:10.5210/fm.v26i7.11565

Micro-celebrities from the North: Young North Korean defectors’ vlogging on YouTube

2021· article· en· W3202086871 on OpenAlexaff
Kyong Yoon

Bibliographic record

VenueFirst Monday · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsAffordanceNegotiationSocial mediaStorytellingDigital mediaMedia studiesDigital storytellingSociologyPolitical scienceAdvertisingGender studiesNarrativeBusinessPsychologySocial scienceArtLaw

Abstract

fetched live from OpenAlex

By analyzing YouTube channels of young North Korean defectors, this article examines the cultural meanings of social media in these marginalized young people’s resettled lives. The article focuses on four YouTube channels managed by four young North Korean defectors living in South Korea. The four channels show how defectors, a majority of whom remain almost invisible in the South Korean public sphere, use digital platforms to display their identity as real people. Moreover, these channels involve digital storytelling of how the defectors negotiate their inter-Korean identities and interact with South Korean viewers. Furthermore, the four YouTube channels reveal how creative labor is professionalized and incorporated into the digital attention economy. This article suggests that, with some restrictions, such as restrictive technological affordances and profit-seeking algorithm, digital platforms allow defector youth to engage with social media storytelling and question the dominant representation of defectors.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.243
Teacher spread0.218 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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