A mixed-methods exploration of how shared athlete leadership influences teamwork
Bibliographic record
Abstract
= 19.9 years). We then used social network analyses to assess the perceived quality of all team members on their task, motivational, social, and external leadership, thereby identifying the four highest-rated athlete leaders whom we invited for interviews in the second qualitative phase. In that second qualitative phase, we conducted one-to-one interviews with athlete leaders. Using deductive framework analysis to analyze all qualitative data, we generated four themes. First, social leadership was believed to lay the groundwork for effective teamwork. Second, task and motivational leadership were perceived to regulate team performance by driving teamwork execution, evaluation, and adjustment. Third, task and external leaders were thought to support or hinder coaches' efforts to facilitate teamwork. Fourth, formal and informal leaders were believed to spread teamwork by setting positive examples for teammates to follow. Overall, our research suggests that the perceived influence of athlete leadership on teamwork is nuanced, with various leadership roles and behaviors impacting teamwork in different ways. Teams could foster effective teamwork by encouraging social and motivational leadership to be shown by all members and appointing high-quality task and external leaders to support coaches' efforts to facilitate teamwork.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".