Media power in digital Asia: Super apps and megacorps
Bibliographic record
Abstract
Tracing global shifts in ownership and conglomeration in the media and technology sectors, this introduction analyzes the emergence of the 'megacorp' and 'super app' as distinct forms and sites of media power. With a focus on Asia, we argue that the pairing of megacorps and super apps is driving the emergence of powerful digital companies that shape social, cultural, and political dynamics worldwide. Through analyses of companies including Reliance, SoftBank, Tencent, Alibaba, and Transsion, this special issue calls for a renewed engagement with theories of monopoly capital via the megacorp, and accounts of consumer and citizen experiences of this monopoly via a quotidian touch point, the super app. In conversation with scholarship on conglomerates, monopolies, and platforms as key institutional forms of media power, we show that media power in this digital conjuncture operates as much through national and regional differences as through the imperative to achieve a global scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".