Investigating the relationships among athlete leadership behaviours, perceptions of athlete leader fairness, and cohesion
Bibliographic record
Abstract
The purpose of the study was to examine athlete leader fairness in relation to athlete leadership behaviours and cohesion. Participants included 363 intercollegiate team sport athletes from Canada and the United States. Participants completed the Leadership Scale for Sports (Chelladurai & Saleh [1980]. Dimensions of leader behavior in sports: Development of a leadership scale. Journal of Sport Psychology, 2(1), 34–45. https://doi.org/10.1123/jsp.2.1.34) to assess athlete leadership, Group Environment Questionnaire (Eys, Carron, et al., [2007]. Item wording and internal consistency of a measure of cohesion: The Group Environment Questionnaire. Journal of Sport and Exercise Psychology, 29(3), 395–402. https://doi.org/10.1123/jsep.29.3.395) to measure cohesion, and a leader fairness inventory (Colquitt [2001]. On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386). Structural equation modelling was used to assess athlete leader fairness as a mediator in the relationship between athlete leadership behaviours and cohesion. Positive relationships were found between athlete leadership behaviours of training and instruction, social support, and positive feedback with dimensions of athlete leader fairness, whereas autocratic behaviour was found to have a negative relationship with athlete leader fairness. Procedural fairness was positively related to all dimensions of cohesion, and interpersonal fairness was positively related to ATG-S. Regarding indirect effects, significant effects were found between athlete leadership behaviours and cohesion through the mediators of procedural fairness and interpersonal fairness. From these findings, procedural fairness was identified as a full mediator between training and instruction and all dimensions of cohesion, and interpersonal fairness was identified as a full mediator in the following relationships: autocratic behaviour-to-ATG-S, social support-to-ATG-S, and positive feedback-to-ATG-S. The present findings provide support for athlete leaders as a source of leader fairness in team sport. The findings also suggest that fairness may help explain why leadership behaviours positively relate to cohesion, indicating that athlete leaders should provide fair leadership to their team members.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".