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Record W1973995009 · doi:10.1080/01436597.2013.802509

Middle Range Powers in Global Governance

2013· article· en· W1973995009 on OpenAlexaboutno aff
Hongying Wang, Erik French

Bibliographic record

VenueThird World Quarterly · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMiddle powerGlobal governanceInternational relationsNeorealism (international relations)MultilateralismCorporate governanceHegemonyWorld orderPolitical scienceInternational relations theoryConstructivism (international relations)Political economyPoliticsSociologyLawForeign policyEconomicsManagement

Abstract

fetched live from OpenAlex

This article compares and evaluates the contributions of middle range powers to global governance initiatives. Examining participation in terms of personnel, financial and ideational contributions, we test several hypotheses derived from neorealism, critical theory, liberalism, constructivism, and post-internationalism against six cases: Canada, Japan, China, Russia, India and Brazil. We find that material power has a negative impact on contributions, while a country’s leadership’s attitude towards the international order, the length of its membership in major international organisations and the strength of its civil society all seem to have positive effects on its participation in global governance. Trade dependence, however, does not seem to exhibit the expected impact. The article indicates that multiple theoretical approaches may prove useful for evaluating the behaviour of middle range powers, and that further research should be conducted on the relative importance of each of the factors mentioned above in explaining middle range power contributions to global governance.

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)0.004
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.007
Scholarly communication0.0040.003
Open science0.0000.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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