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Record W2902866160 · doi:10.1163/18763324-04201005

Risks and Constraints of Sub-Federal Politics in Russia under Vladimir Putin

2015· article· en· W2902866160 on OpenAlexaff
Marat Grebennikov

Bibliographic record

VenueThe Soviet and Post-Soviet Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsConcordia University
Fundersnot available
KeywordsPoliticsPolitical economyAuthoritarianismPolitical scienceRegime changeNationalismState (computer science)CommunismEconomic systemDemocracySociologyLawEconomics

Abstract

fetched live from OpenAlex

Russia’s political system can be best understood as an electoral patronal regime in which key actors are organized into a single pyramid of authority that dominates the political arena, particularly in the ethnic republics. It is argued that the asymmetric federalization of post-Soviet Russia and centralization of governance were stabilizing for the state because, during the tumultuous transition from Communism, they have acted as counterweights to such centrifugal forces as nationalism and religious radicalism. The article addresses this question: Does the political regime under Putin limit the behaviour of regional elites by structuring and prioritizing their agendas or, on the contrary, does this regime gradually devolve to match the underlying political configuration of the state? The article concludes that in multi-ethnic hybrid regimes that preserve contested elections, as does Russia, regional politics matters more than in typical authoritarian regimes. Since Putin’s popularity and power are closely tied to Russia’s economic stability and anti-Western sentiment, protracted economic stagnation coupled with growing social discontent at the regional level will trigger a long-awaited centrifugal change in political authority and may eventually lead to political fragmentation after Putin.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.375
Teacher spread0.274 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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