Who takes the lead? Social network analysis as pioneering tool to investigate shared leadership within sports teams.
Bibliographic record
Abstract
Leaders do not operate in social vacuums, but are imbedded in a web of interpersonal relationships with their teammates and coach. The present manuscript is the first to use social network analysis to provide more insight in the leadership structure within sports teams. Two studies were conducted, including respectively 25 teams (N = 308; Mage = 24.9 years old) and 21 teams (N = 267; Mage = 24.3 years old). The reliability of a fourfold athlete leadership categorization (task, motivational, social, external leader) was established by analyzing leadership networks, which mapped the complete leadership structure within a team. The study findings highlight the existence of shared leadership in sports teams. More specifically, regarding the task and external leadership roles, no significant differences were observed between the leadership quality of coaches and athlete leaders. However, athlete leaders were perceived as better motivational and social leaders than their coaches. Furthermore, both the team captain and informal athlete leaders shared the lead on the different leadership roles. Social network analysis was found to be a pioneering but valuable tool for obtaining a deeper insight in the leadership structure within sports 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".