TO BE CONSIDERED FOR A PUBLICATION IN EFM
Bibliographic record
Abstract
corporate finance team of BNP Paribas and Euronext for their valuable comments and suggestions. We thank Douglas Cummings, and participants at the 2007 Northern Finance Association meetings for helpful comments, and Zhou Zhang and Weimin for excellent research assistance. Mittoo acknowledges funding from the Bank of Montreal Professorship and from the Social Sciences and Humanities Research Council of Canada. 1 Why European firms go public? We survey 78 Chief Financial Officers (CFOs) from 12 European countries about the determinants of going public and exchange listing decisions. The CFOs identify enhanced visibility and prestige, and financing for growth as the most important benefits of an IPO. Their views on other motivations vary across firms and countries. Large firms consider the outside monitoring as the most important benefit, small firms go public primarily to raise capital for growth, and family controlled firms view the IPO as a vehicle to strengthen their bargaining power with creditors without relinquishing control. The English system firms consider the increased share liquidity and the ability to exit as the most important benefits whereas the Italian firms identify the reduction in the cost of capital as most valuable. Despite these divergent views,
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.633 | 0.351 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".