Bibliographic record
Abstract
Summary. This article is devoted to the experience of establishing venture business systems in both developed and developing countries. The paper also covers key issues of main supporting institutions and related challenges. Key words: venture business; venture capital; investments; innovations. Venture capital is an American invention, and so far the United States is home to the largest venture capital industry. This paper presents a study of a number of coun-tries that have tried to develop their own venture capital industries, and issues fre-quently faced by developing and developed nations willing to enhance their domestic venture capital industries. Let’s fi rstly review the experience of developed countries.Canadian government policies have resulted in its venture capital system being based on funds sponsored by labor unions. Recent growth in Canada’s VC market [4] has weakened the union funds’ grip on VC funding, however, and total investment has notably grown. Canada was the world’s fi fth largest recipient of VC fi nancing. Canadian strength in telecommunications technology has been a particular boonto VC investment.In a relative sense, Israel has achieved even greater success, since it was the sixth largest recipient of VC and the world’s largest recipient expressed as venture capital funding as a percent of GDP. At least part of Israel’s success can be traced to deliberate policy decisions by the Likud government in the early 1990s, which took concrete steps to commercialize defense-related technology developed with public funding [2].In Japan, direct investment is not popular, thus there are very few individual inves-tors who invest in start-up or early stage venture enterprises. Because of the lack of individual investors and the conservative investment attitude of venture capital fi rms, the entrepreneur in Japan has to provide a signifi cant portion of start-up capital com-pared with other countries.In contrast with other countries, Sweden was an early mover in venture capital. Although investments were made in Swedish companies earlier, 1973 is considered the year in which the venture capital industry started in a more organised form. Continued focus on R&D in big businesses and in universities mainly resulted in rather limited at-tention being allocated to innovation. The fi rst institutional private equity and venture capitalist, Foretagskapital, was established as a joint venture between the state and merchant banks in Sweden. Soon more funds followed [1].Scoreboard, Denmark, has been the best performer among the global innovation leaders in recent years, although the country is still lagging Sweden somewhat in this area as a whole. A number of Danish venture capitalist companies invested in young start-ups in the early 1980s, thus beginning Denmark’s venture capital tradition [3].Opportunities for entrepreneurs in developing countries are broader in scope than in developed markets, allowing fi rms to pursue a portfolio approach to strategy that can effi ciently manage the higher levels of business and market risk. Entrepreneurs in developing countries face a different set of circumstances than their counterparts in developed economies. While Western entrepreneurs operate at the fringes of the economy, emerging market entrepreneurs operate closer to the core – the needs and opportunities are more widespread.Russia seems to be the worst positioned among the BRICS countries on the innova-tion front. The current problems are far more emphasized by negative demographics
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| 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) | 0.006 | 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".