Public attitudes to China in the ‘Five Eyes': unpacking views across the Anglosphere security community
Bibliographic record
Abstract
China is an important security concern for the United States and its allies, including the Five Eyes intelligence-sharing group that is sometimes described as the core of the ‘Anglosphere’ security community. While we would expect securitising discourses at the elite level to reproduce some common perceptions of China, to what extent are attitudes to China shared across the publics in these countries? In this article, we unpack public attitudes towards China in the United States, the United Kingdom, Canada, Australia, and New Zealand. Drawing on the results of public opinion surveys we conducted in 2022, we note areas of similarity and divergence then drill down into the drivers of public attitudes. We show that even though aggregate attitudes towards China in the five countries appear to align with official security discourses, this hides significant variation in how different groups within these societies view China. In particular, ethnic minorities and recent immigrants, along with members of higher socio-economic classes, urban residents, and young people, tend to be more positive towards China. Our findings bring new insights into the potency of government-driven securitisation, particularly in terms of identifying groups within societies that are less inclined to follow their government’s view of China.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".