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Record W1988834657 · doi:10.1080/01292981003693393

Mobile communication research in Asia: changing technological and intellectual geopolitics?

2010· article· en· W1988834657 on OpenAlexaboutno aff
Jack Linchuan Qiu

Bibliographic record

VenueAsian Journal of Communication · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsPolitical sciencePublic relationsSociologyDiversity (politics)Social scienceLaw

Abstract

fetched live from OpenAlex

Abstract This article reviews Asian mobile communication research since the mid-1990s. First, it identifies key research institutes and funding agencies, not only in Asia but also worldwide public (e.g., the Canadian IDRC) and private (e.g., Microsoft) organizations. It then summarizes the areas of research at micro, meso, and macro levels, including their main topics, methods, and findings, and debates that result from the interaction (and lack of it) among diverse scholarly traditions such as survey, policy analysis, ethnography, action research, and comparative studies. Young as it is, mobile communication research is now a most eclectic area of inquiry, reflecting both the diversity of Asian societies and the growing heterogeneity of communication research itself. What is to remain? Are there centripetal forces that may lead to the confluence of the field in the next 20 years? How does Asian mobile communication research speak to European and American colleagues? Is the trend of changing what Mizuko Ito calls 'technological and intellectual geopolitics' already under way? This article is designed to be first an overview, before more systematic discussions are provided on selected themes of research. The purpose is to piece together the big picture of a burgeoning field in order to identify key development trends and inform future research. Keywords: mobile communicationAsiathematic reviewtechnological and intellectual geopolitics

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.414
Teacher spread0.344 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations16
Published2010
Admission routes1
Has abstractyes

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