Personalization of Trump and Xi in the U.S.–China trade conflict news: Comparison between the U.S. and China
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
News personalization in one-party dominant countries has been understudied or often analyzed through a Western lens. This study unpacked this phenomenon in one-party dominant country with the theory of leadership cult and soft power and compared news personalization of Xi Jinping in China with that of Donald Trump in the U.S. against the backdrop of the U.S.–China trade conflict. This study also investigated the influence of press ideology, political–geographical scope of news coverage and the trade conflict period on the presence and valence of personalization within each country. Results showed that leadership personalization was less prominent in China than in the U.S. The manifestation of news personalization in the U.S. was affected more by press ideology, while contextual factors, such as news political scope and the conflict period, played bigger roles in China. These findings provide insights into how news personalization is displayed in divergent political and media systems.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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