The IPO as an exit strategy for venture capitalists: regional lessons from Canada with international comparisons
Bibliographic record
Abstract
The quantity and quality of innovation capital is essential for the success of the development of knowledge-based growth firms in the twenty-first century (Audretsch, 2007a, 2007b). In this regard, government bodies around the world provide much needed support to entrepreneurs and innovators in their capital raising efforts (see, for example, Cumming, 2007 World Bank, 2004) to facilitate the healthy growth of knowledge-based economies. This support comes in the form of indirect government intervention with tax subsidies and other entrepreneur-friendly regulation (for example, lenient bankruptcy laws and lax securities laws), as well as direct government programs to provide capital for entrepreneurs. One rationale for this support is that there is a perception of the existence of a capital gap for entrepreneurs, since the risks to financing early stage high-technology firms is very pronounced and the rewards not sufficient to entice enough investors. A second rationale is that there are returns to society for having innovation and entrepreneurship. Since the private returns do not account for the social returns, there is an insufficient supply of capital for innovation and entrepreneurship.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".