Increased Opportunities for Private Business as a Direction Vector of Development of the Russian Economy (Case of Volgograd Region)
Bibliographic record
Abstract
The national economy of the Russian Federation would be able to significantly increase annual GDP growth, ifprivate business developed actively, especially small and medium businesses. The authors examined how tosolve this problem in one of the Russian regions - in the Volgograd one. Now Russia's economic policy is aimedat the penetration of market relations in almost all spheres of life, the creation of conditions for their normaldevelopment. However the absolute interaction and separation of functions between the state and marketstructures has not been achieved. They are in constant conflict with each other, thereby exacerbating thedepressive state of the economy. Increase business opportunities will help create for the working population andeconomic conditions that will allow the citizens of its own funds to provide a higher level of social consumption,which includes the best quality services in the field of education and health services, comfortableaccommodation, a decent standard of living in old age. Despite the awareness of solutions aimed at thedevelopment of entrepreneurship, private business faces a number of challenges that make the local authorities.This article describes a number of the problems mentioned above and provides suggestions to increase theopportunities for the development of private enterprise in Russia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".