Media Representation of States Involved in the South China Sea Dispute: International News in Context
Bibliographic record
Abstract
The media has discursively represented China, the Philippines, and the United States as states involved in territorial disputes in the South China Sea. These discursive representations ultimately pervade the media and public spheres. This study aimed to unravel these media representations by employing Halliday’s transitivity analysis and van Djik’s notion of ideological squares in analyzing news articles of the dispute from leading international news media. The analyses uncovered that China, the Philippines, and the United States are depicted to be actively involved in the dispute. The articles depict China’s assertive and aggressive measures in the disputed waters and against the United States. China is likewise portrayed to be favoring efforts to forward diplomatic resolutions in the region. The United States is depicted as aggressive towards China while maintaining a projection of power and intimidation in the region as the security guarantor. The Philippines, moreover, is portrayed to advance its claims in the context of forwarding aggressive policies, diplomatic protest, and negotiations and proposals for diplomatic resolutions, all while balancing relations with the US and China. These discursive representations demonstrate how the media has construed and constructed for the public the states involved in the territorial dispute.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".