FEATURES OF FOREIGN INVESTOR ATTRACTION TO FUNDING OF THE RUSSIAN STARTUPS IN THE CONTEXT OF THE POSTCRISIS ECONOMY
Bibliographic record
Abstract
The current paper considers new opportunities of investing in the Russian business, which became available for investors in terms of the economic crisis. In the period of economic recovery, this topic is especially relevant, as attracting foreign capital to Russian start-ups can accelerate the recovery of the post-crisis domestic economy and contribute the diversification of production in the country. The subject of the study is Russian start-ups, financed by foreign investors, and start-ups in need of attracting capital. The objectives of the work are: identify the reasons for the interest of financing Russian start-ups by foreign investors, study the specifics of the decision on financing, and also to assess the existing risks faced by investors. The study provides a qualitative analysis of the market conjecture, based on estimates of foreign investment market participants, is conducted. Comparison of positions and approaches of investors allows us to find key features and identify problems of investing in Russian start-ups. The arguments for and against investing in the rapidly growing Russian business environment are examined. Among arguments for investing in Russia are cheap labour market and depreciation of start-ups due national currency depreciation, among arguments against - high political and economic risks. The results of this study can be used by investors to make rational decisions on investing in Russian business. Repeated research, extension of the sample and segmentation of data for time periods may reveal changes in key features of investing in Russia in dynamics, and lead to more accurate results.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".