Collective rituals in team sports: Implications for team resilience and communal coping
Bibliographic record
Abstract
Many sports teams engage in collective rituals (e.g., the New Zealand All Blacks’ haka). While the concept has been studied extensively in other fields (e.g., social psychology and cultural anthropology), literature on collective rituals specific to sport is limited. Leveraging theoretical positions and empirical findings from across the human and social sciences, the application of an existing definition of collective ritual in team sports is explored. Complementary research is suggestive of a potential link between collective rituals and two growing topics of interest in group dynamics, namely, team resilience and communal coping. Collective rituals can bolster team resilience by strengthening the group structure and increasing a team’s social capital. They can also serve as communal coping strategies, helping to manage team stressors as they arise. However, at the extremes, collective rituals can become problematic. Over-reliance and abusive rites of passage (i.e., hazing) are considered. Potential applied implications and future research directions in sport psychology are then discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".