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Record W4392059733 · doi:10.1111/1758-5899.13335

Contested informality in regional institutional design: A comparative analysis of <scp>ASEAN</scp> and the Quad

2024· article· en· W4392059733 on OpenAlexafffund
Andrew F. Cooper, Brendon J. Cannon

Bibliographic record

VenueGlobal Policy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEconomic geographyRegional scienceBusinessInternational tradePolitical scienceEconomicsGeography

Abstract

fetched live from OpenAlex

Abstract In terms of institutional positioning, the quartet of Indo‐Pacific states – Australia, India, Japan, and the United States – firmly endorse ASEAN. ‘ASEAN centrality’ is clearly highlighted in all Quad statements. Yet, the Quad presents an organizational and substantive challenge to the core institutional model of ASEAN. This competitive dynamic, with respect to style of associational methods (the how) as opposed to organizational purpose (the why), has not received the scholarly attention it deserves. If the literature does focus on the comparative approaches of ASEAN and the Quad, the prism is for the most part targeted on the differences with respect to the engagement with China. Our analysis is different and emphasizes the contrast between two types of institutional informality exhibited by ASEAN and the Quad. By situating our analysis in the context of contested informality, we point out that both ASEAN and the Quad are signposts showing that the foundational privilege of formal international organizations is under stress, albeit from a wide range of institutional designs. Only by detailing and evaluating the critical divergence in modes of informality can an appreciation of the nature and impact of the contest between ASEAN and the Quad be fully understood.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.006
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.384
Teacher spread0.318 · 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 designQualitative
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 routes2
Has abstractyes

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