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Record W2068983527 · doi:10.1162/0162288054894652

Soft Balancing in the Age of U.S. Primacy

2005· article· en· W2068983527 on OpenAlexaff
T. V. Paul

Bibliographic record

VenueInternational Security · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsMcGill University
Fundersnot available
KeywordsSoft powerCold warSovereigntyHard powerChinaPolitical scienceBalance (ability)Power (physics)Political economyIntervention (counseling)State (computer science)Great powerDevelopment economicsEconomicsLawPoliticsPsychology

Abstract

fetched live from OpenAlex

Analysts have argued that balance of power theory has become irrelevant to understanding state behavior in the post-Cold War international system dominated by the United States. Second-tier major powers (such as China, France, and Russia) and emerging powers (such as Germany and India) have refrained from undertaking traditional hard balancing through the formation of alliances or arms buildups. None of these states fears a loss of its sovereign existence as a result of increasing U.S. power. Nevertheless, some of these same states have engaged in soft-balancing strategies, including the formation of temporary coalitions and institutional bargaining, mainly within the United Nations, to constrain the power as well as the threatening behavior of the United States. Actions taken by others in response to U.S. military intervention in the Kosovo confiict of 1999 and the Iraq war of 2003 offer examples of soft balancing against the United States.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.020
Scholarly communication0.0110.011
Open science0.0010.009
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0110.002

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 head0.015
GPT teacher head0.339
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), 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

Citations502
Published2005
Admission routes1
Has abstractyes

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