Building board expertise through key supporting processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it