Increasing the Efficiency of State Institutional Aid to Small Innovative Enterprises
Bibliographic record
Abstract
This article is devoted to solving the urgent problem of low efficiency of state support system for small innovative enterprises. The development of this sector is essential for the future of Russian economy. The recent economic crisis has only reinvigorated the debate. In generally our paper is concerned with studying the specific factors that influenced a SE’s behavior to be involved in innovation process. This paper elaborates a methodological approach for systematically identifying and estimating institutional factors in the system of small innovative business. The authors have developed a method of assessing the effectiveness of the state support system at the regional level, which makes it possible to evaluate the actual level of performance management in a particular region and to identify existing reserves. Correlation and regression analysis, which allows identifying the most important factors that have the greatest influence on the efficiency of the system of state support in the sector. In addition, proposed correlation and regression models are developed. Application of this method in the practice of public administration of SMEs will take into account the influence of qualitative factors in evaluating the effectiveness of the system as a whole.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".