Examining the leadership behaviours of coaches and athletes: An athlete's perspective
Bibliographic record
Abstract
Leadership can be defined as a process whereby a person influences a group of individuals to achieve a common goal (Northouse, 2010). Not surprisingly, within a sport team, effective leadership has been assigned great value by both coaches and athletes, each crediting the other for their role in contributing to the success of the team. In fact, previous research has shown that coaches and athletes use different types of leadership behaviours (Loughead & Hardy, 2005). However, it should be noted that athletes were asked to evaluate the behaviours provided by all athlete leaders on their team. Another method of examining leadership behaviours is to ask the athletes how they perceive their own leadership behaviours. The participants were 114 athletes competing in a variety of interdependent team sports. The athletes completed the Leadership Scale for Sports (Chelladurai & Saleh, 1980) and were asked to evaluate their own leadership behaviours as well as the leadership behaviours of their coach. The results showed that coaches were perceived by athletes to exhibit training and instruction and autocratic behaviours to a greater extent than athletes. Conversely, athletes were judged to exhibit more social support, positive feedback, and democratic behaviours than coaches. The results are discussed in terms of their implications for understanding the role of leadership from both coaches and athletes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".