Venture Capital in Canada: Lessons for Building (or Restoring) National Wealth
Bibliographic record
Abstract
Canadian policymakers and regulators have been praised for avoiding many of the policy blunders that, when combined with excessive risk‐taking by the banks, nearly brought down the U.S. financial sector. But, as the global economy begins to recover, policymakers everywhere need to find ways to stimulate the creation of new ventures. On that score, Canada's record is not encouraging. The returns on Canadian venture capital investment have been dismally low, particularly in its government‐run funds. In a recent survey, 40% of U.S. venture capital partners identified Canada as having the least favorable treatment of investors of any country they had dealings with. And perhaps most troubling, half of the Canadian corporate executives responding to another survey cited “inability to retain talent” as the biggest threat to their firms. The authors begin by suggesting that these findings are all related. Without investors and the know‐how and networks they bring with them, a country's ability to attract, develop, and retain top talent—business and managerial talent in particular—is significantly reduced. And as the authors go on to argue, the key to building a successful venture capital industry is to match talent with capital in such a way that all three parties—talent, capital providers, and the “matchmakers” who bring together talent and capital—are rewarded for superior performance and held accountable for failure.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".