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Record W2901020568 · doi:10.6000/1929-7092.2018.07.53

Evaluation of Cultural Impact on Regional Economic Development in Russia

2018· article· en· W2901020568 on OpenAlexvenueno aff
Inna Lukashenko, Елена Федорова

Bibliographic record

VenueJournal of Reviews on Global Economics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic base analysisEconomicsWork (physics)Regional scienceEconomic systemBusinessGeographyMicroeconomics

Abstract

fetched live from OpenAlex

Paper is devoted to Dependence of economic development of the Russian regions on their cultural level indicators. It determines and analyzes the influence degree of cultural components of the region development on its economic factors. The investigated statistical base consists of selected cultural and economic indices taken in the period of 2000-2015 years. The hypothesis of significant influence degree of the visits to museums and in particular, theatres number, on the amount of implemented innovational technologies was confirmed. A hypothesis about the close relationship between the volumes of expenditures of the Russian budget for culture and the level of research and development work, as well as directly the number of innovative industrial technologies introduced was confirmed. Hypotheses about the close interaction of cultural indices and such macroeconomic parameters as GDP growth, the volume of the capital investments have not been confirmed. A weak correlation between library holdings volume and the studied economic indicators was noted. The research can be used in design of the regional development programs, in forming budget priorities of budgets projects, or in taking other management decisions programming the basis for effective social and economic policy of the regions.

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)0.008
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.122
GPT teacher head0.422
Teacher spread0.300 · 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 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
Published2018
Admission routes1
Has abstractyes

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