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Record W2107246824 · doi:10.5539/ass.v11n7p59

System of Regions Management in the Circumstances of the Geopolitical Competition: Priorities of the Modernization Development

2015· article· en· W2107246824 on OpenAlexvenueno aff
Popov Alexander Vasilyevich, Volkov Yuri Grigorievich, Guskov Igor Alexandrovich, Khachetsukov Zaur Makhmudovich

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Systems and Logistics Management
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsModernization theoryCompetence (human resources)Competition (biology)Context (archaeology)Flexibility (engineering)BusinessEconomic systemEconomic competitionPolitical scienceEconomic growthEconomicsEconomic policyManagementPolitics

Abstract

fetched live from OpenAlex

The article examines the role of the regions management system in the implementation of modernizationdevelopment in the context of the geopolitical competition. Russian regions welcome modernization, especiallyon the socio-economic level, as they need investments to address the current socio-economic and socialproblems, to borrow advanced technology, and to become spaces of social stability. The authors believe that themodernization project in modern Russia is possible in the context of transition to the regional level, which isassociated both with the modernization capacity of regions management and with the use of regional scenarios ofmodernization development. In authors’ opinion, this allows reducing the financial and organizational burden onthe Federal Center, increasing the responsibility of the management system, and providing its performance withthe necessary degree of competence and flexibility.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.058
GPT teacher head0.239
Teacher spread0.180 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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