VENTURE CAPITAL FUNDING AS A FACTOR OF THE INNOVATIVE DEVELOPMENT
Bibliographic record
Abstract
The paper justifies the necessity to improve the mechanisms of venture capital funding in Kazakhstan for sustainable and effective development of the country. The role of venture capital funding in the innovative development of the countries is investigated on the base of study of the experience of such countries as USA, Canada, Europe, India, and China. The recent research works related to the venture capital funding in different aspects are reviewed. The innovative activity and venture investments in the Republic of Kazakhstan were analyzed. The paper investigates the features of venture capital funding in Kazakhstan. The investigation results show that Kazakhstan system of venture investment is at its initial stage of development, and there are no tangible results of venture field development yet. The conducted research allowed revealing the factors limiting the development of venture investment in Kazakhstan, these are: poor systematic monitoring of funds efficiency invested by the national institutes; lack of effective strategies of venture capital funding; low innovative activity and intensity of venture appearance; uncertainty and gaps in the legislative base related to venture financing; absence of tax concessions and preferences not tied to FEZ or technological parks; absence of strong institutional venture investors; low capacity of securities market and scarcity of its instruments. The work suggests a set of measures directed on activation of venture financing. The implementation of the suggested measures assumes the increased control over the effectiveness of quasi-public structures investments and venture incomes, and creation of conditions for venture capital funding development. The research results can be a cut-off point for further investigations in the field of venture capital funding related to the innovative development of the country.
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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 | 0.002 | 0.012 |
| 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 | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".