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Record W4396964741 · doi:10.1017/nps.2024.26

Great Power Competition, Clientelism, and De Facto States: Transnistria and Taiwan Compared

2024· article· en· W4396964741 on OpenAlexaff
Ion Marandici

Bibliographic record

VenueNationalities Papers · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPost-Soviet Geopolitical Dynamics
Canadian institutionsTD Bank Group
Fundersnot available
KeywordsClientelismDe factoCompetition (biology)Power (physics)Political sciencePoliticsLawPhysics

Abstract

fetched live from OpenAlex

Abstract To what extent can de facto states act autonomously vis-à-vis their patron states and domestic societies? This article draws on theories of clientelism in international relations to develop a novel argument explaining the agency of de facto states. Examining two strategic triangles—Russia–Transnistria–Moldova and US–Taiwan–China—it demonstrates that interrelated domestic factors such as robust political competition, democratic pluralism, reimagined national identities, and big business shape the autonomy of de facto states in Eastern Europe and East Asia. Furthermore, the structured focused comparison of Transnistria and Taiwan indicates that the agency of de facto states declines when rising parent states and dissatisfied patron states challenge the status quo, engaging in great power competition. Their autonomy varies across areas of low and high politics, as patron states prioritize military-security issues and interfere less in the economic and cultural affairs of the de facto 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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0030.002
Open science0.0000.004
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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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