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Record W4409976429 · doi:10.1080/09692290.2025.2493802

Challenging the status quo-revisionist power dichotomy: China and the United States in the trade regime

2025· article· en· W4409976429 on OpenAlexafffund
Kristen Hopewell

Bibliographic record

VenueReview of International Political Economy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of British Columbia
FundersCanada Research Chairs
KeywordsStatus quoChinaPower (physics)EconomicsInternational tradeInternational political economyPolitical sciencePolitical economyDevelopment economicsPoliticsMarket economyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.321
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2025
Admission routes2
Has abstractyes

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