Applying social network analysis to examine the shared nature of athlete leadership within a competitive hockey team: A longitudinal case study
Bibliographic record
Abstract
Research has demonstrated that athlete leadership is a shared practice (e.g., Fransen et al., 2015). That is, numerous team members partake in a team's leadership processes through both formal and informal leadership roles. This research has typically examined athlete leadership using cross-sectional designs, paying little attention to the development and dynamic nature of this construct over time. Using a longitudinal design, the current study used social network analysis (SNA) to examine the shared nature of athlete leadership within a competitive, highly-ranked adolescent male ice hockey team. Specifically, members of the team (N = 20) completed roster-based surveys related to task and social athlete leadership at five time points during the season. Results from two network-based analyses (i.e., network centralization and network density) suggested that the team's task and social leadership became increasingly shared as the season progressed. In particular, the network centralization scores decreased indicating that leadership became more distributed amongst the players and less centralized from Time 1 (task = 32.62%; social = 32.20%) to Time 5 (task = 29.43%; social = 25.90%). Further, the network density scores increased over time indicating that players reported looking more frequently to other team members for leadership at Time 5 (task = 2.20; social = 2.33) than Time 1 (task = 1.81; social = 1.83). The results highlight the value of using a longitudinal design and SNA to examine the shared nature of athlete leadership. Practically, the findings suggest athlete leadership is a shared and dynamic construct that can evolve over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".