Bibliographic record
Abstract
Nineteen venture capitalists are interviewed to understand from their experience- selection, evaluation, and risks factors involved in deciding on life science investments. Results show large and varying support for the theory on venture capitalists ’ role in reducing costs of information asymmetry in investing, as well as, national differences between Canadian and US venture capitalists. The development of life science technologies, particularly biotechnology and medical devices, are probably some of the most intricate, lengthy and complex processes that present themselves today within our global economies and financial systems (Baeyens et al., 2006; Baum and Silverman 2004; DiMasi et al., 2003; Fetterhoff and Voelkel, 2006; Shepard et al., 2003). Biotechnology and medical devices reach into a large and diverse marketplace touching all fields of human knowledge, including pharmaceuticals, food, fuel, as well as our waste product processes. In a sense these technologies encompass some of the most pioneering and valuable creations that people have had the courage to invent (DiMasi et al., 2003; Rousu et al., 2004). In fact, thirty-five percent (35%), or 95 out of 256, of all new therapeutic products that are approved in the past ten years come directly from the life science field
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.609 | 0.417 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".