Outside Directors, Litigation Environment, and Management Earnings Forecasts
Bibliographic record
Abstract
Abstract This study examines whether outside directors factor in litigation costs for the firm while monitoring optimal disclosure policy. It investigates the association of management earnings forecast disclosure and the proportion of outside directors across two regimes with unequal litigation costs, the United States and Canada. I find that the positive association between forecast frequency/ precision and the proportion of outside directors is stronger in Canada. This suggests that outside directors are more likely to encourage disclosure in less litigious Canada. I also find that firm‐level governance mechanisms such as outside directors and country‐level litigation environment act as governance substitutes in determining unbiased forecasts. Specifically, the negative association between forecast bias and the proportion of outside directors is stronger in Canada. I also revisit the effect of legal regime on forecast disclosure in a non‐U.S. context. Recent legislation has increased the likelihood of class‐action lawsuits in Canada. The passage of these laws has decreased the precision in forecasts by Canadian firms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".