Are All Outside Directors Created Equal With Respect to Firm Disclosure Policy
Bibliographic record
Abstract
Empirical evidence on the association between outside directors and firms’ voluntary disclosures is mixed and controversial. We hypothesize that the outside directors do not represent a homogeneous group of people as considered in the literature. Using hand-collected data from a sample of biotechnology firms, we find that the aforesaid association differs based on the directors’ professional backgrounds. Our results are consistent with two ideas. First, an outside director’s influence on firm disclosure policy is shaped by her professional background. Second, firms match outside directors’ professional backgrounds with their disclosure policy. We cannot distinguish between the two explanations. Yet, we make an important contribution to the literature. We show that the impact and the selection prospects of outside directors are not as uniform as previously considered in the literature. Thus, the researchers examining financial disclosures must take into account the background characteristics of all outside directors, not just of those in the audit committee. And investor bodies must consider the background characteristics of candidates in their recommendation for outside-director selection.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".