Ten postulates of a media imperialism framework: For critical research on China’s media power and influence in the Global South
Bibliographic record
Abstract
This article considers the growing media power and influence of China in the global South through the lens of a critical media imperialism framework derived from the geopolitical economy of communications research tradition. While extensive research exists on US media imperialism and the challenge to it posed by China’s global media rise, the idea of China as a media imperialist in its own right has received limited attention. This article addresses this gap in the field by summarizing ten key postulates of a media imperialism framework for critical research on China’s media power and influence in the global South. This article study does not seek to prove China’s status as a new media imperialist power or provide a detailed case study of China’s media imperialism in a specific country. Instead, it synthesizes the ten postulates of a media imperialism framework, examines China’s corporate and state media organizations in relation to these, and draws from relevant scholarship, evidence, and anecdotes. The article argues that each postulate can serve as a foundation for future case studies of different facets of China’s media power and influence in countries across the global South. These postulates can be refined, disproven, or expanded through further research. By considering China’s media power and influence through the lens of the media imperialism framework, this article aims to stimulate further meta-theory, empirical research, and scholarly debates on this pivotal topic, which while contentious, is significant to the future of the global South, and the wider world system.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.076 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".