Director elections: An analysis of shareholder response to directors’ reputation and expertise
Bibliographic record
Abstract
The purpose of this study is to determine whether shareholders take directors’ independence, gender, expertise, and reputation into account when voting in directors’ elections. To this end, we regressed several explanatory variables representing these characteristics on the percentage of “in favour” votes cast during annual elections in 2017 for each director, based on a sample of 60 Canadian firms. Among these explanatory variables, we used two measures of their reputation, one measure of their level of education, several measures of their area of expertise, and one measure of their independence. Their reputation was assessed based on their inclusion in the Canadian Who’s Who directory and their membership on another board of directors of a Canadian public company. The other explanatory variables were collected from official company documents, especially the proxy circulars available on the Canadian Securities Administrators website. The accounting and financial variables were drawn from the Research Insights database. The results of the regression analysis indicate that although shareholders do not seem to consider directors’ reputation and expertise when casting their vote, they do take their independence and gender into account
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".