The Emergence of the Personalized Medicine Innovation Ecosystem in British Columbia: Selective Revealing, Strategic Timing and Success
Bibliographic record
Abstract
Personalized medicine is a growing subsector within medicine and biotechnology, having become a new subdomain of research within the traditional biotechnology industry. This study aims to identify, classify and analyze the emergence of the personalized medicine innovation ecosystem in British Columbia in order to inform innovation policy. We draw on and contribute to the Innovation Ecosystems and the Open Innovation literatures by examining the emergence of the personalized medicine industry in British Columbia, and the commercialization patterns and strategies of the firms within it. In this paper we identify and study the formation, open innovation mechanisms, financing and value creation of companies with technologies related to personalized medicine. Of the 94 PM firms founded in B.C., 64 are currently active, with 48% in therapeutics, 38% in diagnostics, and 14% in digital health. We find evidence of the importance of the Open Innovation mechanisms of selective revealing and of strategic timing to value creation by personalized medicine ventures.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".