Exploring the governance committee: the trinity’s great forgotten
Bibliographic record
Abstract
Purpose This paper aims to explore governance committee’s attributes in terms of composition, roles/duties and responsibilities and operations. Design/methodology/approach Information on the governance committee and the board in general was collected from the websites of 167 Canadian firms. Financial data were collected from the Sedar database. Findings Results uncover two patterns of governance committee attributes (composition, roles and operations), resulting in our characterization of governance committees as “less active” and “more active.” In light of additional analyses, the two groups also differ in terms of antecedents and impact. Practical implications This study can help board members to enhance board effectiveness by describing governance committee attributes and identifying contextual factors that could lead to a more active governance committee. In addition, it suggests that such committee can improve financial performance. Originality/value This empirical research focuses on the governance committee, a largely unexplored primary board oversight committee. An index comprising 19 duties and responsibilities performed by the governance committee was developed.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".