‘Who’ Makes a ‘Good’ Leader? Examining the Influence of Leader Gender with Perceptions of Leader Competency and Employee Outcomes
Bibliographic record
Abstract
Abstract Gender stereotypes suggest men are better fit in roles of leadership. The multi-billion-dollar sport industry is an example of this trend. The extant literature is limited in its examinations of the validity of these stereotypes that negatively influence a woman’s ability to obtain leadership roles in highly men-dominated industries. To address this gap, the current study asked U.S. intercollegiate administrators and coaches to evaluate their supervisors’ leadership capabilities across 15 previously established and validated leadership competencies. The current study sought to better understand the association, if any, of a leader’s gender on their perceived leadership competencies as reported on by their employees. We employed ordinal regression modeling to examine the association between competency evaluations for supervisor gender and the outcome variables of subordinate job satisfaction and organizational commitment. Findings revealed women leaders were rated higher across all leadership competencies by subordinates. Thus, results suggest men are not inherently better fit than women to lead in sport industry settings despite their immense overrepresentation in these leadership roles.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".