Bibliographic record
Abstract
The venture capital (VC) industry plays an important role in nurturing entrepreneurship and innovation, and its role varies from country to country. The six countries whose VC industries are analyzed here are the United States and Canada, whose VC industries are mature; Sweden and Denmark, which have established small but successful VC industries; and Israel and Turkey, whose experiences demonstrate the state of the young VC industry in transition economies. The analysis is based on the four main determinants of the VC industry: sources of financing, institutional infrastructure, exit mechanisms, and entrepreneurship and innovation generators. In addition, the special role of VC financing in the biomaterials industry is explained. Understanding the factors that contribute to the emergence of a successful venture capital industry is important for academics, VC associations, policy-making institutions, government agencies, and investors themselves. How can a country's venture capital infrastructure give it a competitive edge in the global economy? What is the role of VC in the new economy? How have VC industries developed differently in different countries? Are there any lessons for successful VC industry development that can be applied across nations and cultures? How do you measure the maturity of a country's VC industry? The editor and her contributors attempt to answer all these questions, among others. She concludes by offering policy suggestions for countries aiming to establish thriving VC industries of their own.
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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 | 0.016 |
| 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 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".