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Record W2219484242 · doi:10.1080/22041451.2015.1079150

Digital convergence of Korea’s webtoons: transmedia storytelling

2015· article· en· W2219484242 on OpenAlexaff
Dal Yong Jin

Bibliographic record

VenueCommunication Research and Practice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsKorean WaveConvergence (economics)StorytellingTechnological convergenceDigital eraPopular cultureComicsDigital mediaAdvertisingComic stripMedia studiesSociologyThe InternetMultimediaPolitical scienceNarrativeEngineeringComputer scienceBusinessArtWorld Wide WebTelecommunicationsLiteratureEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The webtoon has been one of the major cultural forms representing Korean youth culture due to its convergence of digital technologies, such as the Internet and smartphones, and popular culture – manhwa (comic strips in Korean). While Korea is not the only country to enjoy webcomics, it is the first country in creating a new form of manhwa format by utilizing major characteristics of digital technologies. By employing media convergence supported by transmedia storytelling as a major theoretical framework, this study analyzes the crucial elements characterizing the emergence of the webtoon market. It examines the ways in which webtoons have managed to become one of the Korea’s signature forms of youth culture. Second, it investigates whether webtoons act as one of the major sources for transmedia storytelling. Finally, it maps out whether webtoons utilizing transmedia storytelling take a major role as the primary cultural product of the Korean wave in the global cultural market in the 2010s. This study historicizes the evolution of Korean webtoons according to the surrounding new media ecology, driving the change, and continuity of the manhwa industry over the past 15 years.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.403
GPT teacher head0.503
Teacher spread0.100 · 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

Citations59
Published2015
Admission routes1
Has abstractyes

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