Causes and Consequences of Venture Capitalist Litigation
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
Sticking with an analysis of the situation postdeal, Liu, Paeglis, and Walker take us into the scary world of SEC lawsuits thrown at venture-backed public companies. Does the strong reputation of a venture firm appear to attract litigation due, perhaps, to the deep pockets of the organization? That9s a scary thought and these authors test it. Are the lawsuits frivolous or do they appear to have merit? Do firms continue to fund companies after the imposition of litigation? Do venture firms named in lawsuits tend to keep or lose their reputational rankings? Can you avoid lawsuits by monitoring your portfolio firms more intensively? We thought you might be interested in the answers to some of these provocative questions. <b>TOPICS:</b>Private equity, exchanges/markets/clearinghouses, risk management, statistical methods
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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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