CCTV in Africa: Constructive approach to manufacturing consent
Bibliographic record
Abstract
China Central Television (CCTV) launched its first media centre in Nairobi, Kenya, in 2012 and is one of the main actors in the ‘China’s media go global’ campaign. CCTV-Africa’s reporting style has previously been engaged by media practitioners and academics in terms of its discursive practices. In 2014, a new paradigm studying the journalistic practice of Chinese media in Africa emerged. It has been argued that the journalistic approach deployed by Chinese media in Africa, especially CCTV-Africa, is more constructive than simply positive. This article aims to provide a structural analysis on the role of international news in mediating and reinforcing the ‘harmony of interest’ of transnational elite groups with empirical findings from the case study of CCTV-Africa and its constructive approach of journalism. The findings of this research show that the ‘constructiveness’ of CCTV-Africa is marked with the ‘non-interference’ diplomatic strategy of China in Africa which minimalizes the political involvement of China in local conflicts by reducing investigation on causes and emphasizing solutions. Simultaneously, it also produces an apolitical context which encourages economic development in African societies to cater to the grander politics of China in Africa.
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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 | 0.118 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".