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Record W1553168161 · doi:10.5539/ass.v11n20p90

Innovation as a Vector of Regional Economic Development and a Necessary Condition for the Progress of the World Economy

2015· article· en· W1553168161 on OpenAlexvenueno aff
E. Yakovleva, Natalia Azarova, E. Titova

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsRationalization (economics)HarmonizationIncentiveEconomicsInnovation economicsProfit (economics)Standard of livingPopulationEconomic securityEconomic systemBusinessEconomic growthMarket economySociology

Abstract

fetched live from OpenAlex

Innovative development is an essential component of economic development, responding to global challenges of our time. Please be aware that nowadays there is a great increase of not only the practical implementation of innovative technologies and processes, but also of theoretical understanding of these trends in theoretical studies. Exploring the nature of innovation processes and factors of their incentive economics focuses on the large-scale process of generating ideas and theories, issues of priority, issues of innovation, and economic and cultural backgrounds’ need for innovative ideas. Innovation is the result of the transformation of ideas, research, development, new or improved scientific and technical, socio-economic, political and other decisions that promote, improve the quality and standards of living of the population and national security through the harmonization of the economic interests of economic entities (profit firms), market of consumer interests (needs at the lowest cost) and interests of society (rationalization needs, environmental protection, reduction of unemployment, the growth of the tax base, increase in average household incomes, reducing their differentiation, growth of intellectual potential of society, increase in life expectancy, increasing competitiveness of regions, countries and so on. Therefore, it is necessary to identify the role of innovation in the development of regional and global economy, which this study is dedicated to.

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)0.002
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.047
GPT teacher head0.329
Teacher spread0.281 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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