Why ref? Understanding sport officials' motivations to begin, continue, and quit their avocations
Bibliographic record
Abstract
Developmental trajectories and pathways for athletes and coaches have been frequently examined, whereas sport officials are often overlooked. With attrition rates of up to 30% (Deacon, 2001), sport organizations need to better understand officials' career pathways; what motivates someone to become an official and to stay at it rather than quit? We studied sport officials' ( N = 514) motivations to begin, continue, and quit their craft, categorizing participants as interactors (high number of cues and high interaction with athletes; e.g., soccer referees), monitors (high number of cues and low interaction with athletes; e.g., gymnastics judges), and reactors (e.g., low number of cues and low interaction with athletes; e.g., tennis line judges) (MacMahon & Plessner, 2007). Multiple t-tests and ANOVAs showed that, generally, interactors cited intrinsic reasons, such as enjoyment and passion, to begin officiating, whereas monitors and reactors were more motivated by a sense of being of service or giving back to their sport. For continuing officiating, all officials were most motivated by intrinsic motivations including personal growth and meeting challenges. All officials cited lack of respect, too much stress, and lack of recognition, respectively, as their main beliefs as to why officials would quit the sport. In the discussion, we highlight patterns including the rise of extrinsic reasons to continue refereeing, complimented by the reduction in 'for the sport' reasons. Acknowledgments: The authors would like to thank Denis Auger at Universite du Quebec a Trois-Rivieres for granting us access to this data
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".