Chinese Views on US–China Relations: Continuity and Change across Two US Elections
Bibliographic record
Abstract
Abstract This article examines how Chinese public opinion toward the USA and US–China relations evolved across two US presidential transitions, drawing on original pre- and post-election surveys conducted in 2020 and 2024. We find that perceptions of US global leadership and domestic governance were highly responsive to leadership change, with Biden’s election associated with a marked rebound in confidence, even as views of US global influence grew more polarized. In contrast, attitudes toward bilateral relations remained relatively stable. Respondents consistently acknowledged America’s economic influence while recognizing enduring tensions. At the individual level, younger and more educated respondents expressed more negative views of the relationship but greater confidence in China’s economic trajectory, while those with personal ties to the USA reported more favorable attitudes and stronger endorsement of US values. Beyond these findings, the article advances a theoretical argument that public opinion matters in authoritarian states by sustaining governing legitimacy and, in turn, contributing to regime resilience.
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How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".