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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".