Teammate social ties and subgroup memberships: A season-long social network analysis in track and field
Bibliographic record
Abstract
Sports like track and field have complex interdependence structures, wherein teams are divided by event types with distinct tasks and objectives, while simultaneously sharing a collective outcome. Theorists expect that inherent structural features shape members' interactions by distinguishing teammates whose outcomes are intertwined (Evans et al., 2012). The purpose of this study was to examine: (a) the relationship between athlete centrality and perceptions of team cohesion, and (b) the density of ties among members who share common attributes (e.g., compete in same event). This study followed a Canadian intercollegiate track and field team composed of 113 athletes (49% female) representing four event types. Questionnaires assessed demographics, perceived cohesion, and roster-based nomination items pertaining to social interactions. Data were collected across two waves: (1) early season (n = 78), and (2) postseason (n = 63). At an individual level, beta-centrality and perceived cohesion were measured at each wave. At a group-level, Quadratic Assessment Procedure (QAP) correlations explored how sex and event related to athlete interactions. Results indicated a significant relationship between athlete centrality and perceptions of cohesion (early: r = 0.437 p =
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".