An Exploration of Athletes’ and Coaches’ Perspectives of Fair Athlete Leadership
Bibliographic record
Abstract
This study explored athletes’ (leaders and nonleaders) and coaches’ perceptions of how they conceptualize athlete leader fairness and actions that elicit perceptions of fair treatment and unfair treatment. Participants included intercollegiate varsity athletes ( n = 11) and coaches ( n = 9) who completed a one-on-one interview. Athlete leader fairness was identified as a complex and dynamic phenomenon that is viewed as subjective, situation-specific, essential for effective leadership, and difficult to maintain. Three themes describing athlete leader fairness behaviors were generated: (a) promoting the team’s growth and mission, (b) utilizing leadership power, and (c) being predictable. The present findings formed the basis of a conceptual definition concerning athlete leader fairness. The findings from the present study can be used to advise athlete leaders of how they can provide fair leadership to their team members, as well as inform the development of an inventory for measuring athlete leader fairness.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".