Keeping boards in the loop: getting directors the right information
Bibliographic record
Abstract
Purpose This paper aims to offers suggested practices for dealing with the challenge of providing relevant and reliable information to boards. Design/methodology/approach Notes and reports from formal governance reviews have been considered from five organizations where board information was flagged as a key area for improvement. The cases were chosen from dozens of organizations the author worked with over a 10-year period. Findings The paper explains that boards struggle to process information because of challenges such as group dynamics and cognitive biases. Key themes identified reveal that both the type of information and how it is presented matters to boards. Most significantly, giving more information to boards is not always better. Research limitations/implications This is not an empirical study but instead seeks to use themes identified in practice as the base for suggestions for boards to consider when seeking relevant and reliable information to make decisions. Practical implications This paper makes practical suggestions on how boards and managers can ensure boards receive appropriate information from managers. These include having a clear philosophy for presenting information to the board, being clear on the story that is told, using knowledge visualization when appropriate, explaining how information is relevant to decision items and information items and appointing a steward to oversee the process if needed. Originality/value The struggle around board information has been noted in both research and practice. This paper empowers boards and managers with proactive strategies to steward processes and procedures related to board information.
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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 | 0.096 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".