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Record W2897860296 · doi:10.5539/jpl.v11n4p1

Regional Policy of Russia in the Far East: Why Does It Go Wrong and What Is Apparently Seceded

2018· article· en· W2897860296 on OpenAlexvenueno aff
А.Б. Волынчук, Sergey K. Pestsov, Л. Е. Козлов, Ya.A. Volynchuk

Bibliographic record

VenueJournal of Politics and Law · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsRussian federationContext (archaeology)Strengths and weaknessesRegional policyPolitical scienceInterpretation (philosophy)Subject matterFar EastSubject (documents)Regional scienceEconomic systemEconomicsSociologyGeographyEpistemologyLaw

Abstract

fetched live from OpenAlex

The main subject matter in this article is the development policy of the Russian Far East as one of the aspects (constituent elements) of the regional development of the Russian Federation declared and implemented by the country's leadership since the mid-1990s. The Russian experience of regional policy related to the development of the Far East is of scientific and practical interest. The analysis of modern regional policy allows us talking about a new content interpretation, formation of basic approaches, principles in the development of the Russian Far East. The main features and peculiarities of this policy are considered by the authors in the context of a discussion about the so-called new paradigm of regional policy that is unfolding in recent years. The article analyzes the strengths and weaknesses of the new regional policy based on the provision of tax and other benefits to business entities, and evaluates its effectiveness.

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)0.003
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 score0.015
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0030.002
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 head0.032
GPT teacher head0.314
Teacher spread0.282 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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