The Development of Innovative Startups in Russia: The Regional Aspect
Bibliographic record
Abstract
This paper examines some of the topical issues related to the development of innovative startups in Russia. The authors propose a methodology for assessing the viability of innovative startups, which, if implemented, may help new startups survive their first three years of business. The paper shares the findings of a study of the latest trends on startups both in Russia and overseas, analyzes the degree of activity with which startups are emerging, and explores specific characteristics of entrepreneurs developing their business from scratch, like gender and age. The authors analyze the specificity of Russian practice in terms of developing and implementing the fundamental idea of a startup and provide a rationale for the need to enhance the current legislative framework, which is inhibiting the development of this promising area. The paper also determines the major sources of funding for innovative startups in Russia and shares the findings of a comparative analysis of ratios in the volume of funds borrowed to implement startups. At present, there is a concern about the lack of proper mechanisms for assessing the viability of innovative startups, as well as about the ability to effectively attract outside funding. Among the novel and promising ways to attract investments to help implement startups in the Russian market is crowd-funding. Employing this tool is currently hampered by the lack of proper organizational and legislative regulations respecting this kind of activity. The development of startups in Russia may facilitate boosts in the population’s economic activity levels and help create more jobs. It is to help this cause that the authors have developed a specific methodology for assessing the viability of innovative startups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".