Bibliographic record
Abstract
Purpose Although board expertise has been identified as an important determinant of board performance, some surveys are still reporting that the overall level of board expertise is insufficient to carry out current and emerging roles. Consequently, companies must ensure that board members have the required skills and knowledge. This study aims to examine three board processes aimed at developing and improving board expertise. Design/methodology/approach Based on disclosures in the corporate governance guidelines of 100 leading US companies, the study focuses on three board processes, i.e. director nominations, orientation and education programs, and board performance evaluations. Findings Based on the initial findings, it is found that most companies in the sample were in compliance with stock exchange requirements and provided information on director nominations, orientation and education programs and board performance evaluations. All too often, however, the companies disclosed generic, non‐specific information; this provides little reassurance that the proper processes are in place to promote companies' long‐term interests. Research limitations/implications By examining these key board processes, the paper contributes to the governance literature by providing empirical evidence on this important topic and offering guidance to companies examining board processes aimed at improving directors' overall expertise. Originality/value By focusing on disclosures in corporate governance guidelines, the authors also gain insight into decisions made by companies under increased pressure from securities regulators and other stakeholders to provide increased transparency on governance issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".