Examining the antecedents and outcomes of athlete leader fairness
Bibliographic record
Abstract
Fairness is defined as an individualized perception of an action or statement as appropriate and just according to group rules and standards (Blanchard, 1986; Mallard, Lamont, & Guetzkow, 2009). The purpose of the study was to examine athlete leader fairness in relation to athlete leadership behaviours, cohesion, and athlete satisfaction. Participants were 203 (male n = 113; female n = 90; Mage = 19.85 years, SD = 1.51) intercollegiate team sport athletes. Participants completed the Leadership Scale for Sports (Chelladurai & Saleh, 1980), Differentiated Transformational Leadership Inventory (Callow, Smith, Hardy, Arthur, & Hardy, 2009), Group Environment Questionnaire (Eys, Carron, Bray, & Brawley, 2007), Athlete Satisfaction Questionnaire (Riemer & Chelladurai, 1998), and a leader fairness inventory (Colquitt, 2001). Data were analyzed using path analysis to examine relationships among the variables. Task-oriented leadership predicted procedural fairness (B = .48) and distributive fairness (B = .46), transformational leadership predicted distributive fairness (B = .31), interpersonal fairness (B = .39), and informational fairness (B = .39), and transactional leadership predicted procedural fairness (B = .23), interpersonal fairness (B = .21), and informational fairness (B = .13). In turn, procedural and distributive fairness predicted task cohesion (B = 1.52 and 1.54, respectively), which then predicted satisfaction with performance (B = .40) and the team (B = 1.19). Findings from the present study provide support for athlete leaders as a source of leader fairness perceptions in team sport. Additionally, perceptions of athlete leader fairness are identified as an antecedent of cohesion and athlete satisfaction.
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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 | 0.005 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".