How to recruit female sport officials: A qualitative exploration
Bibliographic record
Abstract
Sport plays an integral role in society and it is unlikely that competitive sporting events would exist without sport officials (e.g., referees, umpires, and judges). Research, however, has shown a decrease in the number of officials (Canadian Heritage, 2013), highlighting the need for evidence-based recruiting strategies. Extant literature mostly focuses on male officials, with little understanding of how to recruit female officials. The purpose of this research was to explore female officials' perspectives on recruiting other female officials. This research is part of a larger study whereby a link to an online survey was sent to officials throughout North America. Our analysis focuses on female officials (N = 990, representing 16 sports) who answered an open-ended survey question, How can we attract more women to officiating? We adopted a pragmatic analytic approach, aiming to generate results that were meaningful for officials and officiating organizations. To achieve this, we performed a content analysis on 40%-60% of participants' responses from each sport. Results revealed three dominant strategies that might facilitate recruitment of female officials: (1) An increase in advertising of women; (2) Increasing the sense of belonging; and (3) Providing more opportunities for mentoring and education. When investigating sport differences, volleyball officials emphasized providing greater incentives as an additional strategy, officials in soccer included giving better assigners/assignments as a strategy, and gymnastic judges gave less thought to increasing the sense of belonging. Discussions will be geared towards practical applications and future directions.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".