Examining the leadership behaviours of athletes in team sports
Bibliographic record
Abstract
Athlete leadership has been defined as an athlete occupying a formal or informal role within a team who influences a group of team members to achieve a common goal (Loughead et al., 2006). This definition highlights two types of athlete leaders: formal athlete leaders (e.g., designated by the coach or their teammates), and informal athlete leaders (e.g., emerges through the interactions that occur within the team). To date, research has found that coaches and athlete leaders exhibit different leadership behaviours (Loughead & Hardy, 2005). However, no research has examined whether the leadership behaviours exhibited by athletes differ based on their leadership role within the team. Therefore, the purpose was to compare the leadership behaviours of formal athlete leaders, informal athlete leaders, and athlete non-leaders. Athletes from a variety of team sports (N = 114) completed the Leadership Scale for Sports (Chelladurai & Saleh, 1980) that assessed their own leadership behaviours. The results of a one-way ANOVA indicated that the behaviours of Training and Instruction and Social Support differed between groups. In particular, formal athlete leaders exhibited significantly greater amounts of Training and Instruction, F(2, 110) = 3.24, p < .05, and Social Support, F(2, 110) = 3.36, p < 0.05, than athlete non-leaders. The results are discussed in terms of their implications for understanding the role of athletes in the leadership that occurs within team sports.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".