What makes a board director better connected? Evidence from graph theory
Bibliographic record
Abstract
We are interested in quantifying and uncovering the relationships that form between the board directors of companies. Using these relationships we compute three network centrality measures for each director in the network and employ them in the analysis of connectedness of directors. Our focus in this study is on the attributes that make a board member better connected. The biological, educational and experiential attributes are used as independent variables to develop a regression model measuring the impact on the three connectivity measures (degree, betweenness and closeness). Our results show that ?Age? has a direct significant impact on all connectedness measures of a board member. We also find that female directors have a higher measure of degree centrality and betweenness centrality, but lower closeness. The number of foreign degrees increases the degree centrality and betweenness centrality but not closeness. The three identified characteristics of ?Age?, ?Gender?, and ?Education? are supporting the idea that a high level of social connection can in part be expected by the characteristics of individual board members and can explain up to 25% of the board member?s connectivity.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".