The IPO as an exit strategy for venture capitalists: regional lessons from Canada with international comparisons
Bibliographic record
Abstract
In this chapter, we present worldwide VC investment, exit and initial public offering (IPO) data up to 2010. Also, we focus on investment activity at the local level with reference to data from Canada and the U.S. We provide statistics which highlight how differences in investment and IPO exit patterns are important for regional prosperity, with particular reference to Canada and Ontario. We consider the VC market in Ontario, Canada, and abroad, drawing on extensive data from the Thompson Financial SDC VentureXpert database. We consider the amount and structure of VC investment in Ontario and the sources of capital, as well as exits from investments. Comparisons are made in reference to economic conditions in the different jurisdictions. We identify empirical proxies for value-added provided by VCs in different regions that have been well-established in the literature as leading to business innovation and commercialization. These proxies include, for example, staging frequency, syndication, and portfolio sizes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".