I'll lead you, you lead me: An examination of athlete leadership behaviours in dyadic sport teams
Bibliographic record
Abstract
The majority of athlete leadership research has examined large interdependent sport teams with little research investigating the leadership behaviours in dyadic teams (Wickwire et al., 2004). This is unfortunate considering empirical evidence has shown that group size can greatly influence group-related factors including leadership (Widmeyer et al., 1990). Therefore, the purpose was to examine the commonalities and differences in athlete leadership behaviours between ice dancing partners. Six individual semi-structured interviews were conducted with three competitive ice dance teams. Using a full range leadership perspective (Avolio, 1999), athletes reported engaging in a variety of transactional (exchange processes between leaders and followers, where followers receive direct rewards for their actions) and transformational (personal, emotional, and inspirational exchanges between leaders and followers, where the goal is to develop followers to their fullest potential) leadership behaviours. Further, the partners indicated sharing the leadership within the team. In particular, the athletes reported the use of both different and complementary leadership behaviours. Based on the findings that emerged from the interviews, there is evidence that partners collectively exerted influence on one another. The results are discussed in relation to the implications of having shared leadership within dyadic teams.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".