Preparing Sport Leaders of the Future To Lead Equitable, Diverse, and Inclusive Sport Organizations: The Insights and Strategies of Professors
Bibliographic record
Abstract
Researchers have documented the tangible and significant benefits to organizations having more diverse senior leadership teams. However, not all industries have embraced his practice. While gains have been made for women securing senior positions in professional sport, the rate of change has been slow, despite the fact that men and women equally aspire to these roles, and women outnumber men in many sport management educational programs. Systemic and structural barriers exist for women seeking senior leadership levels in the industry, a fact that only the women students seem to appreciate (Gray & Weese, 2021). This descriptive study extends this research by engaging sport management professors to determine if they understand the issue and, if so, what they were doing to ensure that their students understand the benefits of equity, diversity, and inclusion (EDI). The professors clearly appreciated the issue and recognized the gender differences that exist between their men and women students. They also shared activities and strategies they use to help ensure that the next generation of sport leaders value and advocate for EDI leadership practices. The professors agreed that they needed to continue to heighten the awareness and sensitivities of their students on the topics of EDI, and they all believed that they could do more to incorporate EDI perspectives in their classes and mentorship sessions. Ten recommendations are provided to assist current and future sport management professors address this critical issue.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".