Personalization of Trump and Xi in the U.S.–China trade conflict news: Comparison between the U.S. and China
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".