A west coast North American comparative study of: venture capitalists' perception risks when selecting life science investment opportunities
Bibliographic record
Abstract
Life science technologies are some of the most comprehensive and difficult to evaluate as investment opportunities. If Venture Capitalists can only invest in a specific number of portfolio investments, it is crucial that the investment opportunities chosen are those that are also the most likely to succeed. Investor perception risks due to lack of qualification, technology comprehension, information asymmetry or overconfidence, are factors that may limit Venture Capitalists from choosing investments that create wealth. To date, little research has been dedicated into the understanding of risk perception and technology comprehension among Venture Capitalists who specialize in life science investment opportunities. This study included a literature review as well as nineteen (19) semi-formal interviews with Venture Capitalists located on the West Coast of Canada and United States. Results show that interviewed Venture Capitalists are, overall, qualified in making life science investment decisions. However, results also showed that there are trends towards Venture Capital adverse selection and overconfidence during investment selection, particularly with respect to personal performance. Results further showed that adverse selection may occur simply as a way of reducing risk, particularly information asymmetry risk. In these instances it appears that opportunity selection is geared towards selection of known and trusted management rather than actual investment opportunity. Differences between West Coast Canadian and United States Venture Capitalists were also found. These differences specifically included the degree to which selection risks are taken, where interviews Canadian Venture Capitalists appeared to be more ethical and cautious investors, and interviewed United States Venture Capitalists were concerned with creating wealth out of innovative technologies in a wide variety of geographical locations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".