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Record W2613300120 · doi:10.22215/cjers.v8i1.2481

Civil Service Reform in Transition: A Case Study of Russia

2013· article· en· W2613300120 on OpenAlexaffvenue
Svetlana Inkina

Bibliographic record

VenueThe Canadian Journal of European and Russian Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBureaucracyPresidencyPublic administrationElitePoliticsCivil servicePolitical sciencePublic serviceState (computer science)CommunismGovernment (linguistics)Law

Abstract

fetched live from OpenAlex

Public administrative and civil service reforms have widely been used as a popular strategy to bring about systemic changes in entrenched bureaucracies. The general tendency that occurred in Post-Communist states was to adopt comprehensive policy measures dealing with the efficiency and effectiveness of state apparatus. This paper examines the process of an attempted civil service reform in Russia starting from the first term of Putin's Presidency. Based upon interviews with experts and senior public officials, it elaborates on the role of leadership, or the willingness of the national political elite to improve the system of public administration; the impact of path-dependency upon the course of institutional transformation; and finally, the role of reform strategy in the policy implementation process. The article concludes that the case of civil service reform in Russia may be explained by a combination of policy-making variables listed above. In addition, it highlights the transformation of the Russian policy-making system during the years of political centralization. Full text available at: https://doi.org/10.22215/rera.v8i1.222

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)0.002
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.005
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.318
Teacher spread0.255 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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