Venture-Capital Exits in Canada and the United States
Bibliographic record
Abstract
Abstract Venture capital exit vehicles enable, to different degrees, mitigation of informational asymmetries and agency costs between the entrepreneurial venture and the new owners of the firm. Different exit vehicles also affect the amount of new capital for the entrepreneurial firm. Based on these factors, we conjecture the efficient pattern of exits depending on the quality of the entrepreneurial venture, the nature of its assets, and the duration of venture capital investment. We empirically assess the significance of these factors using a multinomial logit model. Our comparative results between Canada and the U.S. provide insight into the impact of different institutional and legal constraints, and suggest such constraints have distorted the efficient pattern of exits in Canada. Contents I NTRODUCTION ……………………………………………………………………………………………..3 I. E XIT V EHICLES ………………………………………………………………………………………...8 II. A G ENERAL T HEORY OF V ENTURE C APITAL E XITS ……………………………………………………10 III. E
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".