Less- Versus Well-Developed Venture Capital Networks: The Venture Capital Acquisition Process in New Brunswick
Bibliographic record
Abstract
Whilst azeas with well-developed Venture Capital (VC) networks, such as Silicon Valley and Boston's Route 128, have been extensively investigated, areas with less-developed networks have received much less attention. This is somewhat surprising given that these areas are often in need of promoting entrepreneurial activity in order to stimulate regional economic development. This study has sought to fill this gap by suggesting a framework for describing the venture capital acquisition process, not just in well-developed but also in less-developed networks. The framework sought to examine the network environment in the context of system characteristics, government and people factors as well as processes, and was applied to the venture capital acquisition process in New Brunswick. Ten entrepreneurs and six venture capitalists were interviewed on 20 VC negotiations that they had been involved in. Findings reveal that the framework allows a determination of the state of development of a network. For New Brunswick, it showed the region to be of a mixed nature. Because the framework points to specific aspects of the network environrent and processes, it constitutes a useful tool for policymaking.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".