‘Who’ Makes a ‘Good’ Leader? Examining the Influence of Leader Gender with Perceptions of Leader Competency and Employee Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it