No emotion is an island: an overview of theoretical perspectives and narrative research on emotions in sport and physical activity
Bibliographic record
Abstract
Within sport and physical activity settings emotions have typically been conceived and explored from an individualistic or intrapersonal perspective, although researchers are increasingly turning their attention to the interpersonal aspects of emotions and emotion regulation. In this paper, we provide a theoretical overview of the social or interpersonal aspects of emotions from a psychological perspective, and we also consider theoretical perspectives of emotion as intersubjective, social, performative and embodied. We then provide a review of narrative research on emotion in sport and physical activity contexts and provide suggestions for future research in this area. We suggest that narrative approaches can advance research on emotions in sport and physical activity by exploring how emotions arise within the context of social relationships; by exploring how emotional stories or narratives function and are used by athletes, coaches, and others within sport and physical activity contexts; by examining how emotions are created, recreated, and sustained through the stories people tell; by examining how collective and group-based emotions are intertwined with one’s identity and identity development; and by highlighting the ways in which social and cultural narratives within sport shape athletes’ emotional experiences. We conclude by describing some challenges we have faced in conducting qualitative research from a narrative lens, and we describe how we have navigated these issues as a way of offering some ‘lessons learned’ from our own research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".