Bibliographic record
Abstract
(Excerpt) The debate over executive pay has gripped corporate law scholars, regulators, and the national public for decades. A C-Suite position provides uniquely lucrative financial benefits to executives, especially to Chief Executive Officers (CEOs). Over the past few decades, CEO pay has risen spectacularly, as has debate regarding why this has occurred and whether policy should or can correct it. The reasons why CEO pay has increased exponentially in the last 30 years are complex, and the solutions for reigning in executive compensation have been incomplete at best. Yet one glaring fact about the C-Suite eludes much of the corporate governance literature and executive compensation policy reforms and proposals: the C-Suite, particularly the CEO role, has long been and continues to be dominated by men. Despite making up half the workforce, few women lead companies in corporate America. Less than 6% of CEOs of Fortune 500 companies are women, and women make up less than a quarter of C-level executives. Furthermore, few of the executive positions that traditionally lead to CEO roles are occupied by women. As the Wall Street Journal reported in 2020, for women, “the barrier isn’t only a glass ceiling at the very top, but also an invisible wall that sidelines them from the kinds of roles that have been traditional stepping stones to the CEO position.” Instead, women find themselves in “pink collar” C-Suite roles, such as head of human resources or legal. Not only do such positions rarely lead to the top, but they rarely constitute the highest compensated positions within corporations. Ample evidence shows that when women come to dominate a profession, the salary of that profession drops. This is particularly true in high-paid white-collar jobs. With this Essay, we pose the question of whether the opposite proves equally true: as men dominate a profession, does the salary of that profession rise? As corporate scholars, we seek to understand how the pinnacle of the corporation—the CEO position—is both male and uniquely well-remunerated. Might masculinity be the culprit behind increasingly outrageous CEO compensation packages? This Essay begins to explore the correlation between executive compensation and men’s domination over senior executive roles, focusing on the CEO position. We delve into various theories that could help explain why men dominate the most lucrative role in corporate America. We argue that law and corporate governance need to account for these theories in designing solutions that address gender disparity in the CEO role.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".