Commentary: Sticking to the Knitting: CIHR, Innovation and Canadian Biotech
Bibliographic record
Abstract
The novel proposal outlined by Glenn Brimacombe suggests that the federal government directly participate in funding incremental venture capital investment in Canadian biotechnology, with the goal of facilitating commercialization of Canadian biotechnology and health sciences intellectual property. In this way, they suggest, the economic development benefits of the Canadian current investment in health sciences will be increased. The proposal is based on two premises that need further evaluation: (1) the biotechnology sector in Canada presently underperforms in terms of value creation; (2) this underperformance is due to inadequate venture capital investment. It is the author's view that, although several measures do suggest relative system underperformance, this is likely due to structural differences rather than inadequate venture capital investment. The absence of large, integrated, global biopharmaceutical firms based in Canada, the large number of very small biotech firms and the absence of a clear federal policy mandate supporting technology transfer and underinvestment in public sector funded basic research may all be contributory factors. Given the Canadian biotech sector's current efficiency at creating value from limited public investment in basic science, increasing the core CIHR budget might be an even better investment opportunity for limited incremental funding.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.106 | 0.071 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".