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Record W7117581146 · doi:10.1177/17480485251406017

South Korea's network media economy: Growth, concentration and upheaval, 2010–2022

2025· article· en· W7117581146 on OpenAlexaff
Dal Yong Jin, Seoyeon Park

Bibliographic record

VenueInternational Communication Gazette · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRestructuringOligopolyThe InternetMedia industryRevenueMarket concentrationDigital economyDecentralizationDigital transformationDigital media

Abstract

fetched live from OpenAlex

This article examines the growth and structural transformation of South Korea's network media economy from 2010 to 2022 as part of the Global Media and Internet Concentration Project. Using CR4 and Herfindahl–Hirschman Index measures, it analyzes revenue trends and market concentration across telecommunications, media services, and core internet sectors. Despite overall industry growth, concentration levels declined due to the rapid expansion of digital cultural industries. However, oligopolistic control persists in the telecoms sector, while global platforms and major domestic players are increasing their influence in online media services. As a result, Korea's media economy has become increasingly dualized, characterized by concentrated infrastructure sectors alongside diversified media services. The article highlights that rapid industrial expansion does not necessarily ensure the decentralization of market power, and it calls for critical perspectives on market concentration and industrial restructuring in the digital age.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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