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Record W4413025677 · doi:10.1080/01436597.2025.2532006

‘Authentic’ multilateralism and the stigmatisation of ‘small circles’: China, India, and the contestation over institutional design

2025· article· en· W4413025677 on OpenAlexaff
Andrew F. Cooper

Bibliographic record

VenueThird World Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsBalsillie School of International AffairsUniversity of Waterloo
Fundersnot available
KeywordsMultilateralismChinaPolitical sciencePolitical economyDevelopment economicsSociologyPoliticsLawEconomics

Abstract

fetched live from OpenAlex

This article explores the contestation over institutional design between China and India as a crucial barometer of fragmentation regarding the conceptualisation and practice of multilateralism. With respect to conceptual framing, the article focuses on China’s promotion of ‘international discourse power’ in terms of the advocacy for an ‘authentic’ multilateralism. With respect to practice, the article shifts the examination of the contest over the nature of institutions away from the vertical ‘rising/revisionist’ hierarchically contextualised challenge (externally from the Global South), with specific reference to the China–United States rivalry, to the horizontal competition (internally within the Global South) located in the China–India relationship. At odds with older images of a common legacy-driven Bandung spirit, the focus is on the negative side of a competitive dynamic. On the one hand, the article analyses the scope and intensity around Chinese stigmatisation of India’s ‘small circle’ activities. On the other hand, the article privileges the range in the repertoire of India’s responses, from non-response to deflection, counter-stigmatisation, and validation. The article analyses this contest across a wide range of institutions, including G7 outreach to the Quadrilateral Security Dialogue, the Brazil, Russia, India, China, South Africa (BRICS), and the G20.

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)0.001
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.010
GPT teacher head0.260
Teacher spread0.249 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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