Bibliographic record
Abstract
Objective. The objective of the research is to identify the state and features of the world venture business development. Methods. Following methods and techniques of knowledge are used in the process of the study: theoretical generalization and comparison, analysis and synthesis, induction and deduction, grouping, and classification. Results. According to the results of the study, it is found that the number of venture funds in the world is increasing; the largest number of venture funds operates in America, including the USA; the most famous and reputable venture funds are: Intel Capital, Google Ventures, Salesforce Ventures, Comcast Ventures, Qualcomm Ventures, Cisco Investments, Santander InnoVentures, Ascension Ventures, CyberAgent Ventures, SBIInvestment, SMBC Venture Capital etc.; the volume of venture investments in the world in 2012-2019 increased significantly — almost in 6 times; venture investments are not equally distributed by stages of development of innovative entrepre- neurship — the largest volumes of venture investments in 2012-2019 are attracted to «Seed stage» and «Early stage»; it is recorded not only an increase in total venture capital, but also the average size of venture capital per project; the most attractive for venture investors are startups operating in such areas as: software, fintech, pharmaceuticals and biotechnology, consumer goods and recreation; the world leader in terms of venture capital is the United States; the most attractive countries for venture investors are the United States, the United Kingdom, Canada, Hong Kong, Japan, Singapore, Australia, Germany, New Zealand and Denmark; since 2014, large companies have been venturing mainly not through their own venture funds, but by joining global venture funds — an average 30 % of the total number of projects supported by venture funds. The practical significance of the results obtained is in the possibility of their use in designing a development strategy and increasing the competitiveness of Ukraine's innovation infrastructure.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".