Government support of small and medium sized entrepreneurship in Russia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it