How venture capitalists decide which new medical technologies come to exist
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
To encourage the commercial translation of biomedical discoveries, public policies increasingly seek to stimulate the venture capital industry. Very little is known, however, about the way venture capitalists assess the likely benefits new technologies may bring to clinical practice and healthcare systems. Drawing on a five-year fieldwork conducted in Quebec (Canada), which included in-depth interviews and document analysis, we explore why capital investors choose to invest in certain health technology-based ventures and how they influence the innovation process. Our findings clarify how capital investors: first, use market-oriented valuations when they pick and ‘coach’ technology entrepreneurs; second, act to transform and protect their investments; and finally, exert their authority along the technology development process. Current innovation policies should be carefully examined because capital investors’ understanding of the world in which they operate largely determines which health technologies make their way into healthcare systems and which may never come into existence.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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 it