Venture Capital and Vulnerability: Navigating Natural Disasters and Investment Resilience
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
ABSTRACT This study examines the impact of natural disasters on venture capital (VC) investment decisions. Using 47 catastrophic natural disasters occurred in the United States from 1990 to 2019, our empirical analysis reveals a significant reduction in VC investments in disaster zones. Additionally, natural disasters negatively influence VC exit strategies, reducing the likelihood and extending the time to successful exits via IPOs. However, we find that green VCs are more likely to invest in disaster‐affected areas, indicating potential resilience through green technological innovation. Our findings emphasize sustainability and disaster mitigation, and offer valuable insights for policymakers and investors amidst rising climate uncertainties.
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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 | 0.000 | 0.001 |
| 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