Bibliographic record
Abstract
Introduction by Dilek Cetindamar New Directions in Venture Capital Venture Capital and the Innovation Process by Jesper Lindgaard Christensen Angel Networks for the 21st Century by Benoit Leleux, Julian Lange, and Bernard Surlemont The Role of Venture Capitalists in Going Private Transactions by Arman Kosedao National Experiences The Determinants of Venture Captial Funding: Evidence Across Countries by Leslie Ann Jeng and Phillippe C. Wells The Swedish Venture Captial Industry--an Infant, Adolescent or Grown-up? by Dilek Cetindamar and Staffan Jacobsson The Rise, Fall, and Possible Sustainable Re-vitalisation of the Danish Venture Captial Market by Jesper Lindgaard Christensen Venture Captial in Canada: a Maturing Industry, with Distinctive Features and New Challenges by Charles H. Davis Israels Venture Captial Industry: Emergence, Operation, and Impact by Gil Avnimelech and Moriss Teubal Fostering Innovation Financing in Developing Countries: The Case of Turkey by Dilek Cetindamar Industry Experiences The Role of Venture Captial Financing to Young Biomaterials Firms by Annika Rickne Conclusion by Dilek Cetindamar
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".