Government support of small and medium sized entrepreneurship in Russia
Bibliographic record
Abstract
Support of the small and medium sized entrepreneurship (SME) sector is recognized to be one of Russia’s economic policy priorities2, 3. It is customary to speak of that sector’s low level of development compared with other countries. However, when comparable estimates are applied, the gap does not appear to be catastrophic. The relative share of SMEs in the value added produced by Russia’s business sector amounts to about 44 percent, in the developed countries – OECD member states it amounts on average to 55 percent, in the USA – to 48 percent, and in Canada – to 30 percent. The problems faced by Russian SMEs, in qualitative terms, are as follows: the percentage of exporters and technological startups is low, and a greater part of that sector is unregulated; in 2018, the relative share of medium sized firms and the number of technological startups shrank even further. The conditions for and specific features of the SME sector’s development vary across Russia’s regions, and this fact is completely overlooked by prevailing legislation. According to our estimations, entrepreneurial activity in the regions does not depend on government support, instead responding to macroeconomic and institutional changes. In 2018, in a majority of Russian regions, the number of SME subjects and their turnover declined in response to shrinking personal income, especially in the regions with a high relative share taken up by the shadow sector, while the same indices increased in those regions that hosted the FIFA World Cup events.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".