‘Authentic’ multilateralism and the stigmatisation of ‘small circles’: China, India, and the contestation over institutional design
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".