An interpretive analysis of the social functions of emotions in sport
Bibliographic record
Abstract
There is increasing attention to interpersonal aspects of emotions and emotion regulation in sport (Friesen et al., 2013; Tamminen & Gaudreau, 2014), yet researchers have rarely explored athletes' perceptions of the functions of emotions within team and group settings. The purpose of this research was to explore athletes' accounts of the social functions of emotions in sport. Team (n = 9) and individual (n = 5) sport varsity athletes (50% female, age range: 18-26 years) each participated in two semi-structured interviews. Interpretive data analysis consisted of coding, categorization, and thematic organization (Mayan, 2009). Athletes reported individual and communal stressors, which were distinguished by: (a) the extent to which the stressor affected the entire team; (b) the role of the athlete(s) affected by the stressor; and (c) the origin of the stressor (e.g., academic vs. sport). Athletes described experiences of individual, group-based, and collective emotions, and they also reported emotional conflict when they simultaneously experienced individual and group-based or collective emotions. With respect to the social functions of emotions, participants indicated that emotional expressions impacted team functioning and performance, communicated team values, and served affiliative functions among teammates. Emotions also prompted communal coping to deal with stressors as a team. Athletes' emotional experiences, expressions, and communal coping were influenced by social relationships with teammates, and by leaders and coaches. Based on these findings, framed within a growing body of literature, emotions are not 'individual' phenomena. Rather, emotions occur within the context of interpersonal relationships, and emotions have social and performance consequences in sport.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| 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".