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Record W7131235961 · doi:10.14258/epb202562

THE BASIS FOR THE FORMATION OF RELEVANT INFORMATION ON THE ACTIVITIES OF SMALL BUSINESSES AT THE NATIONAL AND REGIONAL LEVEL

2025· article· ru· W7131235961 on OpenAlexaboutno aff
I. N. Sannikova, M. V. Laskina

Bibliographic record

VenueEconomics Profession Business · 2025
Typearticle
Languageru
FieldAgricultural and Biological Sciences
TopicAgricultural and Financial Auditing
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Small businessSet (abstract data type)State (computer science)Sustainable developmentQuarter (Canadian coin)Key (lock)Information system

Abstract

fetched live from OpenAlex

The article reveals the features of assessing the performance of small enterprises to determine the possibilities of their sustainable development and providing support at the regional level. The purpose of the study is to determine a key set of financial indicators based on simplified accounting reports and to expand the scope of explanations for the reports to form relevant information on the activities of small enterprises, which is necessary to understand the trends in the development of small businesses. The research methodology is based on logical and epistemological tools using general scientific methods of comparison. The study is conducted in a movement from the general to the particular, and from the particular to the general. As a general deductive principle, the significance of small business activities for the national and regional economy is considered, and as a particular principle, the requirements for the formation of small business information are considered. The structure of employees in small enterprises in Russia as a whole corresponds to the general structure of employment in significant aspects, which allows us to conclude that relevant information for small and medium-sized enterprises regarding the assessment of employment, the labor market, and the social component of ESG assessments should correspond to general economic practices and trends. Making up about a quarter of Russia's economy, small and medium-sized enterprises should be fully characterized by financial and non-financial performance indicators, without which it is impossible to assess the current state of the economy and predict the country's socio-economic development. Regional databases on the state of small enterprises must have specific characteristics related to the structure of the regional economy. The necessary information should be generated on a systematic basis as part of the reporting of small enterprises.

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)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.010
Science and technology studies0.0020.002
Scholarly communication0.0060.008
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.059
GPT teacher head0.236
Teacher spread0.176 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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