Alignment and Divergence in Government and Private VC: The Amplifying Role of Geopolitical Risks
Bibliographic record
Abstract
This paper investigates the impact of geopolitical situation on the dynamics between Government Venture Capital (GVC) and Private Venture Capital (PVC) investments. Exploiting the US-China trade war since 2018, we observe that PVCs in China became less likely to invest in GVC-funded startups under heightened geopolitical tensions, and the effect is stronger for more prominent GVCs. However, in areas with significant government subsidies such as high-tech industries, GVCs' involvement serves as a compelling signal to private investors such that the negative impact is mitigated or even reversed. Our findings contribute new insights to the fields of strategic management and international business by elucidating how venture capital strategies adapt in response to geopolitical risks. The study also provides valuable guidance for practitioners and policymakers navigating complex global investment landscapes.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".