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Record W3124741531 · doi:10.1093/isq/sqaa092

Does Institutional Proliferation Undermine Cooperation? Theory and Evidence from Climate Change

2021· article· en· W3124741531 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Studies Quarterly · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsConcordia University
FundersRobertson Foundation
KeywordsPaceArgument (complex analysis)Join (topology)Work (physics)State (computer science)Climate changePoliticsPolitical scienceEconomic systemPolitical economyBusinessSociologyEconomicsLawComputer scienceGeographyEngineering

Abstract

fetched live from OpenAlex

Abstract Global politics has undergone a tremendous institutional proliferation, yet many questions remain about why states join these new institutions and whether they support cooperation. I build on existing work to develop a general theory of state participation in dense institutional environments that also helps to explain cooperative outcomes. I argue that states may be dissatisfied when cooperation proceeds either too slowly or too quickly and that these two types of dissatisfaction motivate opposing participation behaviors. Deepeners are states that are dissatisfied with the slow pace of cooperation and join institutions to support cooperation, while fragmenters are states dissatisfied with the quick pace and join institutions to undermine cooperation. I evaluate my argument using new data on sixty-three climate institutions and states’ greenhouse gas mitigation targets in the Paris Agreement on Climate Change. I find that membership in climate institutions designed to facilitate implementation is associated with more ambitious targets, while membership in general is unrelated to targets.

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.

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)0.000
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: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.147
GPT teacher head0.320
Teacher spread0.173 · 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