Stance in News Discourse: Analysis of Two News Reports in Daily Newspapers in China and the US
Bibliographic record
Abstract
The recent Sino-US trade disputes add to the long list of economic and political conflicts between the two largest economies in the world. However, although a trade war is now put on hold with the two countries continuing their negotiations, a different war is fought by major news and business press in both countries to justify the actions taken by each side and gain support from the international community. It therefore becomes a topic of interest as to how the news media make deliberate language choices to influence their readers with their stances and attitudes. This study compares two news reports in daily newspapers in China and the US on a significant trade dispute between China and the US: US imposing safeguard duties on tires from China in 2009. Through Transitivity and Modality analysis, this study aims to demonstrate how news media from rival countries make language choices to help reconstruct events and how different stances and attitudes are implied so as to manipulate the readers to interpret information in an intended way.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".