Catalyzing capital for Canada's life sciences industry
Bibliographic record
Abstract
Canada's biotech sector ranks within the top five globally, but its life sciences venture capital (VC) industry is among the worlds weakest. This makes for an interesting case study in understanding the disconnect between low levels of VC and a healthy innovation ecosystem in terms of R&D spending, skilled workforce and enterprise support. Three key provinces (Quebec, Ontario and British Columbia) that have taken significantly different approaches to attracting VC are large enough to attract as much government investment as whole emerging markets. The aim of this article is to present evidence from a Canadian natural economic experiment in order to evaluate the effectiveness of varying government policies in attracting VC investment, to illustrate how these policies need tailoring to individual sub-sectors of the life sciences sector, and to highlight potential policy mechanisms that may be applicable beyond Canada's borders. We employ VC returns on investment (ROI) and exit data as a proxy for our evaluation. Our results suggest that government biotechnology investment needs to be structured end-to-end from early to late stage in order to be successful, that prevalence of private and international VC flows is critical for generating market efficiency, and that there is an ‘optimal’ efficient amount of capital before ROI result in diminishing returns.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".