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Record W3120075154 · doi:10.5539/jpl.v13n4p41

2020 Constitutional Reform and Center-Region Relations in Russia

2020· article· en· W3120075154 on OpenAlexvenueno aff
Ekaterina Kudryashova, Rustam M. Mirzaev

Bibliographic record

VenueJournal of Politics and Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentConstitutionDecentralizationPolitical scienceRepresentation (politics)State (computer science)Public administrationFederalismFederal stateCenter (category theory)LawPolitics

Abstract

fetched live from OpenAlex

This article examines 2020 constitutional reform in Russia. The amendments recently were approved on July, 1st by nation-vide vote. The purpose of the article is to analyze center-region trends in Russia and how they are reflected in the proposed constitutional amendments. The prospective changes in regional representation in the federal decision-making process shall also be discussed. The article shows how chaotic decentralization during the 90-s was replaced by a trend of centralization. The 2020 constitutional reform reflects a further strengthening of this centralization trend. The problems faced by the Russian regions are left unresolved. It is becoming an overcentralized country. The amended Russian Constitution envisages a reformed role for the Russian State Council. It is supposed to be defined in the Constitution as a consultative body by the President and is expected to be formed of regional leaders. At the same time, the Federal Council of the Federal Assembly (the upper chamber of the Russian parliament), which represents the different constituencies, is also set to be reformed. However, after the reform some of its powers shall be restricted. The changes to regional representation in the federal decision-making process has a controversial and inconsistent character and inevitably supports the general trend towards centralization.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.318

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 communication0.0000.000
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 head0.036
GPT teacher head0.322
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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