The political duality: On the advantages and disadvantages of ex-politicians and former government officials serving on boards of directors
Bibliographic record
Abstract
In this study, we examine two key issues situated at the intersection of corporate governance and corporate political activity literature. The first is whether the presence of ex-politicians or former government officials on a corporate board provides a competitive advantage for the firm. A second, related question is whether the presence of these outside directors on the board of directors is perceived as desirable by their fellow directors. While some have characterized the study of board processes as a black box (Leblanc, 2003; Pugliese et al., 2009) due to the difficulty in acquiring data, we circumvented this challenge by directly surveying 82 Canadian board members, then delved deeper with ten directors using supplemental qualitative interviews. The results were examined via the lens of strategic positioning theory in contrast to the well-worn use of agency and resource dependency theories in the literature. Our findings suggest that heterogeneous benefits may accrue depending upon the industry involved, and the political experience of the director(s) in question. However, a majority of current directors expressed significant reservations concerning the appointment of a political director. These findings, combined with the understudied Canadian context and the use of qualitative research methods, contribute to the extant literature.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".