Venture Capitalists and COVID-19
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
We survey over 1,000 institutional and corporate venture capitalists (VCs) at more than 900 different firms to learn how their decisions and investments have been affected by the COVID-19 pandemic. We compare their survey answers to those provided by a large sample of VCs in early 2016 and analyzed in Gompers, VCs have slowed their investment pace (71%of normal) and expect to invest at 81% of their normal pace over the coming year. Not surprisingly, they have devoted more time to guiding the portfolio companies through the pandemic. VCs report that 52% of their portfolio companies are positively affected or unaffected by the pandemic; 38% are negatively affected; and 10% are severely negatively affected. Overall, they expect the pandemic to have a small negative effect on their fund IRRs (-1.6%) and MOICs (-0.07). Surprisingly, we find little change in the allocation of their time to helping portfolio companies relative to looking for new investments. In general, we find only modest differences between institutional and corporate VCs.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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