Mobile communication research in Asia: changing technological and intellectual geopolitics?
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".