Emotions in context – social aspects of emotions in sport settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it