American and Chinese Public Opinion in an Era of Great Power Competition: Ingroup Bias and Threat Perceptions
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
As the US-China great power competition intensifies, public opinion polling may help gauge internal drivers of foreign policy decision-making. Using Pew Research Center data, the authors analyzed how American and Chinese respondents viewed their own and each other's countries between 2008–2016. They further examined how American attitudes towards China varied by political affiliation between 2008–2019. Both Americans and the Chinese displayed ingroup bias (i.e. rating their own country more positively than the other) and viewed China as a challenger to US hegemony. However, while the Chinese exhibited higher levels of ingroup bias overall, there was no evidence of increasing bias over time. Meanwhile, Americans showed increasing ingroup bias, primarily due to their souring evaluations of China, a tendency that was strongest among Republicans.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it