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Record W4403510117 · doi:10.1177/23477970241282065

The Sino-Indian Rivalry and Balance-of-power Theory: Explaining India’s Underbalancing

2024· article· en· W4403510117 on OpenAlexaff

Bibliographic record

VenueJournal of Asian Security and International Affairs · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsMcGill University
Fundersnot available
KeywordsRivalryBalance (ability)Power (physics)EconomicsPolitical sciencePsychologyMacroeconomicsPhysicsNeuroscience

Abstract

fetched live from OpenAlex

Balance-of-power theory has been challenged as insufficient for explaining state behaviour. Powerful anomalies for the theory exist, especially among states confronting intense rivalry and war. One such anomaly is underbalancing in the Sino-Indian rivalry by the Indian side up until 2017. Today India is still engaged in limited hard balancing, relying on asymmetrical arms build-up and strategic partnership with the United States and Japan that are not equal to military alignment. This article argues that India has occasionally engaged in hard balancing, relying on arms build-up and limited alliance formation, but in general, there has been a serious effort not to resort to intense hard balancing by forming military alliances or symmetrical arms build-up. This calls for an explanation. The core argument I make is that the type of balancing is intimately related to the type of rivalry states have. The China– India rivalry has yet to become an intense existential variety compared to the India– Pakistan rivalry where existential security and protection of national identity are of major concern. Indian elite’s perceptions of the non-existential character of the Chinese threat and their reading of the Chinese strategy towards India have been the primary factors in explaining India’s balancing response. In the latter, active hard balancing has been occurring both internally and externally, whereas the former is characterised by a combination of limited hard balancing, soft-balancing and diplomatic engagement, components of a hedging strategy. The hard balancing has picked up momentum since 2017 in response to a more assertive strategy of the Xi Jinping regime as the Chinese government has ratcheted up military activity on the India–China border. The general implication is that rivals who do not fear existential threats need not engage in intense hard balancing. Perceptions of the threat level play a bigger role in what kind of balancing behaviour occurs in international politics than acknowledged in standard theories on balance of power, especially of the automatic balancing variety.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.241

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 studies0.0000.000
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 head0.008
GPT teacher head0.293
Teacher spread0.284 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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