Honour versus face: a psycho-cultural approach to US-China great power politics
Bibliographic record
Abstract
This article presents a psycho-cultural framework for analysing US-China relations in the context of the great power competition, explaining the escalation of their rivalry and the proliferation of conflict. It emphasises the critical role of self-esteem and culture in shaping domestic and international dynamics, focusing on the interplay between honour and face as distinct expressions of self-esteem in US and Chinese societies. The study illustrates how pursuing national self-esteem drives confrontational interactions between these two powers, with the 2018–2020 US-China trade war as a prime example. Here, nationalist narratives were crafted to evoke emotional resonance with domestic audiences, intensifying the conflict beyond mere economic competition into a struggle for national self-esteem. The rise of identity politics in both countries has further exacerbated tensions, as perceived disrespect has triggered coercive responses, worsening bilateral relations.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".