Taking mediated stance via news headline transediting: a case study of the China-U.S. trade conflict in 2018
Bibliographic record
Abstract
This article studies mediated stance in the transedited news headlines on the 2018 China-U.S. trade conflict. It draws on Appraisal theory developed by Martin and White (2005) to examine the transeditor’s stance via an analysis of 66 English news headlines and 50 Chinese headlines. The English texts were collected from the American mainstream media, while the Chinese texts were chosen from China’s major presses. The result of the analysis shows that when news headlines are transedited from English to Chinese, stance mediation normally sounds negative towards the U.S. and positive towards China. The investigations also found that the selected Chinese presses predominantly showed heteroglossic patterns in the mediated stance they took while the English media tended to use monoglossic ones. It is argued that possible reasons for such stance deviation may include ideological tendencies of the media, different readerships and their expectations of the American and Chinese media, and the different socio-cultural beliefs between the two countries.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".