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Formation of the State Regional Policy: The Experience of the G7 Countries

2021· article· en· W4205335662 on OpenAlexaboutno aff
Н. Подлужна, V. S. Dolha

Bibliographic record

VenueBusiness Inform · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationUkrainianRegional policyTransparency (behavior)Strengths and weaknessesDocumentationState (computer science)BusinessAdaptation (eye)Political scienceEconomic policyPublic administrationRegional scienceGeography

Abstract

fetched live from OpenAlex

The possibilities of adaptation of the experience of the G7 countries in the conditions of Ukraine regarding the regulatory and legal support for the formation and implementation of the State regional policy are determined. Methods of analysis and synthesis of information were used, which allowed to establish advantages and «weaknesses» in the legislation of both Ukraine and the G7 countries to ensure the development of the regional economy, also a comparison method based on which the possibility of adaptation of foreign experience in the formation and implementation of the State regional policy was determined. The main directions of implementation of the State regional policy were established through a detailed study of the existing regulatory and legal documents of Ukraine. Advantages and «weaknesses» in the legislation of Ukraine on regulation of regional development are identified. It is defined that the «weaknesses» of Ukrainian legislation in the direction of regulation of the State regional policy are the following: development and implementation of generalized programs for the development of regions without taking into account the level of development of territorial units; weak stimulation of innovations; lack of transparency of the activities of the State administration bodies and control over documentation as to local budgets; lack of provisions to increase the level of development of depressed regions. The possibility of using the experience of the G7 countries to develop recommendations for improving the State regional policy are substantiated. It is determined that the United Kingdom, France and Italy do not have special legislation on regional policy, but it is effective in the United States, Canada, Japan and Germany. The experience of using instruments for the implementation of the State regional policy by the G7 countries in the absence of special legislation was studied. The normative and legal documents of the State regional policy of the G7 countries, which have special legislation on the development of regions, were monitored. A comparative analysis of Ukrainian and foreign experience on implementation of regional development policy was carried out. Recommendations on accelerating the processes of stimulating the development of regions by activating regional policy towards supporting depressed regions are proposed as follows: support for small and medium-sized businesses; introduction of smart specialization with simultaneous development of diversified forms of knowledge economy; introduction of instruments of innovative and digital economy in all spheres of activity of economic entities; inclusion of priorities of small towns and villages into the regional development plans.

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)0.007
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
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 head0.031
GPT teacher head0.233
Teacher spread0.201 · 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
Published2021
Admission routes1
Has abstractyes

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