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Record W2991550914 · doi:10.1086/720647

Peaceful Neighborhoods and Democratic Differences

2022· article· en· W2991550914 on OpenAlexaff
Mark David Nieman, Douglas M. Gibler

Bibliographic record

VenueThe Journal of Politics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAllianceDemocracyArgument (complex analysis)EndogeneityGeopoliticsPolitical sciencePolitical economyPoliticsSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Democracies are thought to behave differently from other states, particularly when cooperating in international institutions, such as alliances. We argue that these democratic differences depend on geopolitical environments that make cooperation possible. Although studies have demonstrated endogeneity between democracy and peace, few analyze the effects of this joint relationship on democratic differences. We explore this argument using the alliance literature and argue that the empirical finding that democracies are more reliable is driven by the tendency of democracies to cluster in peaceful environments. Alliances are more likely to be “scraps of paper” when found in more dangerous environments. By jointly modeling regime type and political environment using data on alliance termination from 1920 to 2001, we show that alliance reliability is a function of a threat environment. Our argument has important ramifications for a host of literatures focused on regime type, as well as current debates over the effectiveness of democratic deterrence.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.027
GPT teacher head0.298
Teacher spread0.271 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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