The impact of demographic variables on recruitment and retention: Insights from Canadian basketball referees
Bibliographic record
Abstract
Sport officials play a vital role in ensuring athlete safety, enforcing rules, and maintaining fair competitions. Since sport officials are integral to organized sport, understanding factors that affect their participation and performance is crucial. Previous research has investigated the influence that organizational support, job satisfaction, and self-efficacy have on sport officials’ careers. However, little is known about the demographic characteristics (e.g., sex, age, race, geography, etc…) of sport officials that influence their recruitment and retention. Through a secondary data analysis, we investigated if Canada Basketball referees’ demographic characteristics influenced response patterns in self-reported aspects of their entry to officiating, as well as their retention. Participants included 398 registered basketball referees (86.4% male, 11.8% female) from across Canada. The referees’ average age was 51.8 years, with 67.8% of the sample having 9 or more years’ experience. Referees who had 9 or more years of experience were more likely to be Caucasian males. Compared to males, females were more likely to retire within 3 years, despite being more satisfied with advancement opportunities and receiving assignments that improved their skills. Further, referees at lower competitive levels were more likely to be motivated to become referees to address the officiating shortage, though they had lower self-reported competence and social connections with other referees. The findings provide a nuanced understanding of recruitment and retention dynamics in basketball referees, offering valuable recommendations for Canada Basketball to enhance their recruitment and retention strategies—which could be applied to officials from other sports as well.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".