A Season-Long Examination of Team Structure and Its Implications for Subgroups in Individual Sport
Bibliographic record
Abstract
The authors explored how sport structure predisposed a team to subgroup formation and influenced athlete interactions and team functioning. A season-long qualitative case study was undertaken with a nationally ranked Canadian track and field team. Semistructured interviews were conducted with coaches (n = 4) and athletes (n = 11) from different event groups (e.g., sprinters, jumpers) at the beginning and at the end of the season. The results highlighted constraints that directly impacted athlete interactions and predisposed the group to subgroup formation (e.g., sport/event type, facility/schedule limitations, team size/change over time). The constraints led to structural divides that impacted interactions but could be overcome through team building, engaging with leaders, and prioritizing communication. These findings underline how structure imposed by the design of sports impacts teammate interactions and how practitioners, coaches, and athletes can manage groups when facing such constraints. The authors describe theoretical and practical implications while also proposing potential future directions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".