МИРОВОЙ ОПЫТ ФОРМИРОВАНИЯ СИСТЕМЫ МЕР ПОДДЕРЖКИ МАЛОГО И СРЕДНЕГО ПРЕДПРИНИМАТЕЛЬСТВА
Bibliographic record
Abstract
В статье проанализирован мировой опыт формирования систем поддержки малого и среднего бизнеса. Автором исследованы тенденции изменения доли сектора малого и среднего предпринимательства в ВВП государств БРИКС и G7. С применением инструментария корреляционно-регрессионного анализа проведена оценка влияния интенсивности развития МСП на эффективность функционирования экономических систем (на материалах стран БРИКС и G7). Раскрыты особенности подходов к формированию мер поддержки МСП, применяемые в разных странах. Выделены ключевые характеристики наиболее популярных и действенных страновых моделей поддержки МСП, таких как: американо-канадская, латиноамериканская, германо-австрийская, южно-корейская, японская, китайская. Дана оценка данным страновым моделям с точки зрения их содействия росту эффективности развития малых и средних предпринимательских структур в государстве. Автором сделаны выводы о том, что для повышения эффективности системы поддержки МСП в Российской Федерации целесообразно продолжить работу по снижению административной нагрузки на бизнес, развивать механизмы финансовой поддержки начинающих предпринимателей, создавать и развивать инфраструктуру поддержки предпринимательства, активнее использовать международный опыт и сотрудничество для обмена лучшими практиками и разработки новых подходов к поддержке малого и среднего бизнеса. The article analyzes the world experience in forming systems of support for small and medium businesses. The author studies trends in the change in the share of the small and medium business sector in the GDP of the BRICS and G7 countries. Using the tools of correlation and regression analysis, an assessment of the impact of the intensity of SME development on the efficiency of economic systems (based on the materials of the BRICS and G7 countries) was carried out. The features of approaches to the formation of SME support measures used in different countries are revealed. The key characteristics of the most popular and effective country models of SME support are highlighted, such as: American-Canadian, Latin American, German-Austrian, South Korean, Japanese, Chinese. An assessment is given to these country models from the point of view of their contribution to the growth of the efficiency of development of small and medium business structures in the state. The author concludes that in order to increase the efficiency of the SME support system in the Russian Federation, it is advisable to continue working to reduce the administrative burden on business, develop mechanisms for financial support for start-up entrepreneurs, create and develop an infrastructure for supporting entrepreneurship, and more actively use international experience and cooperation to exchange best practices and develop new approaches to supporting small and medium businesses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.023 |
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".