Motives for becoming an ice hockey official
Bibliographic record
Abstract
Sport officials are often maligned, abused, and ridiculed; yet, their presence on the playing surface is indispensable. Understanding why officials become involved in such a negative environment becomes necessary if we are to retain and attract individuals into this essential role. The purpose of this study was to examine ice hockey officials' motives for becoming involved. For this purpose, 22 hockey officials (7 women and 15 men), were interviewed using a semi-structured interview format and asked why they started officiating. It was found that financial motivation was a major encouraging factor in becoming an official as 54.5% mentioned this motive. The influence of relatives, friends, and coaches was another major motivating factor for entering into an ice hockey officiating career (45.5%) with more women citing this motive (85.7%) than men (27%). Other motivational factors detected in the study were the desire to stay involved with the sport (40.9%) which men stated (53.3%) more often than women (14.3%) despite the fact that every official had participated in hockey as an athlete; the desire to stay active (13.7%); and also motives related to skill development (13.7%). It appears that there are intrinsic, extrinsic, and socially-related motives for becoming an ice hockey official that may differ between genders. Implications for recruitment and retention, given the potential for gender differences, will be discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".