THE BASIS FOR THE FORMATION OF RELEVANT INFORMATION ON THE ACTIVITIES OF SMALL BUSINESSES AT THE NATIONAL AND REGIONAL LEVEL
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".