Taking mediated stance via news headline transediting: a case study of the China-U.S. trade conflict in 2018
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it