Challenging the status quo-revisionist power dichotomy: China and the United States in the trade regime
Bibliographic record
Abstract
There has been intense debate about whether China is a ‘status quo’ or ‘revisionist’ power in the international system. Since the first Trump administration, prompted by the American turn to aggressive unilateralism and assault on international institutions, many have questioned whether it is in fact the United States (US) that is the revisionist power. In this article, I argue that this debate rests on a false dichotomy that fails to recognise the changeable, multivalent and contested nature of international norms, rules and principles. The article draws on analysis of the trade regime, a key pillar of global order and a site where US-China conflict has been particularly destabilising. As I show, the US and China have each been able to present their conflicting positions as derived from established norms and principles, while portraying the other as a threat to the system. Existing debates about revisionist versus status-quo powers miss the fact that there are multiple, conflicting norms in existing governance regimes, and the rules of many regimes are contested and evolving, rather than fixed and static. Understanding the impact of contemporary power shifts on the liberal international order therefore requires a more nuanced and accurate understanding of how its constituent institutions actually operate.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".