An exploration of the developmental sport and training histories of Canadian sport officials
Bibliographic record
Abstract
Sport officials occupy essential roles in sport and are necessary for sport to function properly. However, compared to athletes and coaches there has been scant research conducted on the development of sport officials. Therefore, the purpose of this thesis was to explore the developmental pathways and milestones that might relate to success as an official. A sample of 223 Canadian sport officials completed The Developmental History of Officials Questionnaire, which collected information on sport and officiating participation histories, as well as training histories related to officiating. Results suggest that respondents??? highest level of athletic performance was predictive of a similarly high level as an official (H(3, n = 217) = 13.37, p < .01, ??2 = 0.06), thus past athletic participation might be beneficial for future officials??? development. Additionally, starting at a younger age as an official was also predictive of reaching a higher level as an official (F(3, 212) = 9.09, p < .001, ??2 = 0.90). Competitive officiating was the most relevant activity for skill development, with national/international level referees consistently officiating more hours throughout their career, while practice activities were not as prevalent. Future studies should attempt to increase the sample size, widen the variety of sports represented, and gather more respondents from lower- and middle-tier officiating backgrounds.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".