Strategies to recruit and retain sport officials should differ based on officials' sex
Bibliographic record
Abstract
Competitive sports would not exist without sport officials (i.e., referees, judges, and umpires), yet recent evidence highlights unsustainable rates of officiating attrition. For instance, 80% of new officials quit within three years of beginning (Montanaro, 2019) and there was a 38% drop in active Canadian sport officials from 1998 to 2010 (Canadian Heritage, 2013). It is not surprising, then, that sport officiating organizations have sought new methods to recruit and retain officials. It is unknown, however, whether the efficacy of recruitment and retention strategies might differ based on sport officials' sex. Using a secondary data source, the purpose of the present study was to compare male and female sport officials' responses to an online survey on recruitment and retention. Participants included 18,706 (1,799 female) sport officials representing 16 sports. The survey yielded nominal and ordinal data, which were analyzed using Crosstabs and Mann-Whitney U tests, respectively. Compared to male sport officials, females began officiating for different reasons (e.g., money, social opportunities, and giving back to their sports), had different experiences as officials (e.g., less likely to have friends as officials and less likely to believe their training was adequate), and had different motivations for quitting (e.g., lack of time, high expenses, no mentorship, and poor training). Since results have connections to autonomy, competence, and relatedness, the discussion is framed in Self-Determination Theory (Ryan & Deci, 2000). Implications for sport officiating organizations and researchers will be explored.
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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 | 0.030 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".