Women’s Leadership in the Sports Industry
Bibliographic record
Abstract
Sports have proven to have multiple benefits for the development of both girls and boys. Accessibility and the resources available to young girls and women to participate in sports is often inadequate in comparison to that available for their male counterparts. Female leadership within the workforce is increasing yet the male dominated sports industry seems to be stagnant in following this societal trend. The purpose of this exploratory study was to highlight and examine the barriers faced by men compared to women in achieving leadership positions in sports management and coaching. Using an interview method, the experiences of eleven participants within the sports industry were explored. Participants were comprised of U SPORTS and RSEQ (Réseau du sport étudiant du Québec) leaders and included six head coaches, two young assistant coaches and three athletic directors (totaling five men and six women). It is evident that women in the industry experience more barriers to achieving leadership positions. Six themes emerged, with additional sub-themes exposed. The six main themes uncovered were mentorship, role models, experience in sports, staying true to oneself, barriers and solutions. The results highlight the need and importance to invest time and money in women’s sports and the need to educate various stakeholders on the value and benefits that come with sports participation. In addition, it is crucial that those holding top rank positions (majority men) provide opportunities for women and allow these appointees to truly be their authentic selves while functioning in leadership positions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".