Using social network analysis to examine cohesion and leadership in a CIS varsity team
Bibliographic record
Abstract
Social network analysis (SNA) is a valuable tool to measure the bonds between athletes and can provide insight into the complexity of relationships amongst team members. Intra-group relationships are critical for sport teams and can include aspects such as cohesion and leadership. Historically, the majority of research examining cohesion and leadership has used questionnaires that provide information about the team as a whole. However, to be concerned with intra-group relations, a focus on actual relations between team members is equally important. Thus, the purpose of this study was to use SNA to examine the relations between team members in order to better understand aspects concerning cohesion and team leadership. Varsity curling team players (N = 5) provided information about their social network ties to other team members with regards to how task and socially cohesive they felt towards each teammate, as well as on task and social leadership skills for each teammate. Total degree centrality analyses, scores ranging from 0 to 1, were conducted and showed that the skip was responsible for holding the team together from both a task (.69) and social (.64) cohesion perspective compared to the rest of the team (.48, .55 respectively). As for leadership, results indicated that the skip and second were viewed as team leaders for both task (.56, .44) and social (.53, .47) leadership compared to the rest of the team (.28, .30). Results may be explained by the fact that the skip and second were the most skilled and oldest players.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".