Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<JATS1:p>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.</JATS1:p> <JATS1:p>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.</JATS1:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it