Evidence-Based Strategies to Enhance Women Sport Officials’ Recruitment and Retention
Bibliographic record
Abstract
Sport plays an integral role in society, but competitive sporting events would not exist without sport officials. Research has shown a decrease in the number of qualified sport officials, highlighting the need for evidence-based recruitment and retention strategies. Extant literature mostly focuses on men sport officials, with little understanding of how to recruit and retain women sport officials. Using a secondary analysis, the purpose of this research was to explore women sport officials’ perspectives on recruiting and retaining other women sport officials. Participants ( N = 495) responded to the question, “How can we attract more women to officiating?” A content analysis of written responses yielded four themes: (a) Promoting Officiating to Women, (b) Greater Inclusion and Respect for Women Officials, (c) Educational and Mentoring Opportunities for Women Officials, and (d) Offering Incentives and Additional Support for Women Officials. These results highlight the importance of implementing specific recruitment strategies suggested by participating women officials to help increase and promote women officiating in sport.
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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 | 0.086 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".