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Record W2120545463 · doi:10.5539/ass.v11n19p247

Social Function of Small Business Taxes in Russia

2015· article· en· W2120545463 on OpenAlexvenueno aff
Владимир Владимирович Глухов, Egor Vladimirovich Glukhov, Zhanna Ivanovna Lialina, Vladimir Anatolievich Ostanin, Yuri Vladimirovich Rozhkov

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsIgnoranceBusinessFunction (biology)Order (exchange)PopulationWork (physics)Small businessEconomic systemIndustrial organizationPublic economicsEconomicsMarketingFinancePolitical science

Abstract

fetched live from OpenAlex

The article deals with the national-level problem of development and support of the Russian small business. It emphasizes that in this area the financial tools and methods that have been well proven in other countries are not yet implemented in full. It is concluded that the development of the theory and practical application of scientific concepts is necessary, which would allow a more active involvement of the population in this sphere of entrepreneurial business. There presented the evidence that the incomplete and incorrect statistics about the volume of resources accumulated in small business and entrepreneurs’ legal ignorance result in an underestimation of the role of taxes, as the limited financial base does not allow small business to attract professionals responsible for making effective financial decisions to work. The authors’ recommendation may be used during the formation of the tax system, capable to support and develop entrepreneurial activity at both the nationwide and regional levels. This system should include the development of tax measures in order to enhance the social and economic development of the Russian economy, while adapting the positive experience of foreign countries to the conditions of Russian reality.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.057
GPT teacher head0.304
Teacher spread0.246 · 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 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

Citations3
Published2015
Admission routes1
Has abstractyes

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