Emotions in context – social aspects of emotions in sport settings
Bibliographic record
Abstract
Emotions are an ubiquitous component of sport experiences (Jones & Uphill, 2012). Much research has been directed at uncovering relevant antecedents, moderators, and consequences of emotional responses to sport participation, performance, and outcomes. What is surprising, however, it that this research has focused almost exclusively on individuals as isolated actors in their sport experiences. Yet, neither athletes nor coaches or officials operate in a social vacuum. On the contrary, social influence is a pervasive feature of sport (Beauchamp & Eys, 2014). In the present symposium, we address emotions as products of their social context and provide examples of how this context influences their development, character, and regulation. In the form of five theoretically-framed presentations including a mix of quantitative and qualitative methodologies across diverse samples capturing adolescence and adulthood, we focus on (a) social factors as causes of emotions, (b) self-conscious emotions as socially constructed phenomena, (c) a framework for collective emotions, (d) experiences of individual versus collective emotions, and (e) interpersonal emotion regulation. The symposium ends with a discussion, highlighting the influence of the social context on emotional experiences in sport and guidelines to advance theory, research, and practice. Beauchamp, M. R., & Eys, M. A. (Eds.) (2014). Group dynamics in exercise and sport psychology (2nd ed.). Abingdon, UK: Routledge. Jones, M., & Uphill, M. (2012). Emotion in sport: Antecedents and performance consequences. In J. Thatcher, M. Jones, & D. Lavallee (Eds.), Coping and emotion in sport (2nd ed., pp. 33–61). Abingdon, UK: Nova Science.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".