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Record W2623153980 · doi:10.1177/0163443717709441

Construction of digital Korea: the evolution of new communication technologies in the 21st century

2017· article· en· W2623153980 on OpenAlexaff
Dal Yong Jin

Bibliographic record

VenueMedia Culture & Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBoomThe InternetSociocultural evolutionTelecommunicationsEmerging technologiesDigital mediaInformation and Communications TechnologyDigital transformationPhenomenonGlobeBroadbandEngineeringPolitical scienceWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

This article is to document digital Korea. It discusses the sociocultural contexts of the growth of digital technologies and culture because the current boom of digital technologies and culture cannot be separated from each other. Although it cannot go back several decades, it starts to address some early developments of the Internet directly influencing the contemporary Korean society. This historicization process allows us to firmly comprehend several key developments, in particular, the major reasons for development and the implications of the digital Korean phenomenon. While there are several digital technologies, it analyzes a few major digital technologies, such as the Internet, broadband services, and smartphone technologies, as well as relevant digital culture, from three different lens, including information and communication technology policies, corporate competition, and cultural perspectives.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.006
Scholarly communication0.0080.009
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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