Exploring emotions as social phenomena among Canadian varsity athletes
Bibliographic record
Abstract
Objectives Athletes are constantly engaging with teammates, coaches, and opponents, and rather than treating emotions as manifested in the individual as is often the case, psychological analyses need to treat emotions as social and relational. The purpose of this research was to explore athletes' accounts of emotions as social phenomena in sport using qualitative inquiry methods. Method Fourteen Canadian varsity athletes (7 males, 7 females, age range: 18–26 years) from a variety of sports participated in two semi-structured interviews. Data were analyzed using inductive coding, categorization, micro-analysis, and abduction (Mayan, 2009; Strauss & Corbin, 1998). Results Athletes reported individual and shared stressors that led to individual, group-based, and collective emotions, and they also reported emotional conflict when they simultaneously experienced individual and group-based or collective emotions. Emotional expressions were perceived to impact team functioning and performance, communicated team values, served affiliative functions among teammates, and prompted communal coping to deal with stressors as a team. Factors which appeared to influence athletes' emotions included athlete identity, teammate relationships, leaders and coaches, and social norms for emotion expression. Conclusions Our study extends previous research by examining emotions as social phenomena among athletes from a variety of sports, and by elaborating on the role of athletes' social identity with regard to their emotional experiences 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 | 0.002 | 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 | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".