Bibliographic record
Abstract
ABSTRACT Research Question/Issue I study the expected liability cost of cross‐listing into the United States by examining the change in the structure of a Canadian firm's directors' and officers' liability insurance contract (D&O insurance) before and after cross‐listing on an exchange located in the United States (NYSE, NASDAQ, or OTC). Research Findings/Insights Results show that neither the likelihood of having D&O liability insurance increases significantly only when the NASDAQ is the chosen as the cross‐listing venue nor the amount of coverage changes significantly after cross‐listing. With respect to choosing the NYSE as the cross‐listing venue, results show that coverage does not increase, but the premium does. As a result, the D&O insurance premium per dollar of coverage increases significantly only when the firm cross‐lists on the NYSE. A robust point estimate is that a Canadian firm's D&O liability insurance premium increases by 40%–60% when it becomes listed on a US market. Theoretical/Academic Implications D&O insurers adjust their expected litigation costs as a function of where shares are traded not because of the severity of damages paid in the event of litigation, by mostly because of an increase in the frequency of such litigation. Practitioner/Policy Implications If D&O premium‐to‐coverage ratio allows one to measure a company's litigation risk, then there would be value to investors to have access to basic D&O insurance information such as the premium and the coverage.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".