How potential knowledge spillovers between venture capitalists' entrepreneurial projects affect the specialization and diversification of VC funds when VC effort has value
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Research concerning diversification and specialization of venture capital funds typically does not consider how a VC's effort might influence performance of different portfolios. We develop a model that analyzes VC effort when there is the potential for cross‐sectional and/or serial knowledge spillover among projects. The model generates two implications concerning VC effort and performance. First, VC post‐investment effort is a nonmonotonic function of performance shocks, especially for diversified VCs. Second, greater cross‐sectional and serial knowledge spillovers improve the performance of specialization relative to diversification, and shape how the number of decision stages in a project affects portfolio choice. Copyright © 2011 Strategic Management Society.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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