A conceptual framework for collective emotions in sport
Bibliographic record
Abstract
In spite of preliminary evidence of mood linkage among athletes of sport teams over the course of a competition as well as relations to subsequent performance (Totterdell, 2000), research on collective emotions in sport has received scant attention in recent years. The purpose of this presentation is to advance a preliminary conceptual framework for collective emotions in sport with the intention to structure and stimulate future research. To this end, we follow recommendations by Rocco and Plakhotnik (2009) and turn to relevant work in general psychology, sociology, and philosophy. Drawing on this work, we define collective emotions and present five potential causal pathways, ranging from the individual- to a society-level, namely Identification, Contagion, Conformity, Shared Stimuli, and Socialization. We suggest that each of these pathways is distinct in terms of its underlying conceptual basis, mechanisms (mediators), facilitators, operant levels, duration, and the involvement of others. We propose, however, that most pathways are moderated by athletes' ability and motivation to perceive and process relevant social cues. Based on this framework, we classify previous work, predict the salience of different pathways across situations, and provide methodological strategies to advance the further empirical investigation of collective emotions in sport. Rocco, T. S., & Plakhotnik, M. S. (2009). Literature reviews, conceptual frameworks, and theoretical frameworks: Terms, functions, and distinctions. Human Resource Development Review, 8, 120–130. Totterdell, P. (2000). Catching moods and hitting runs: Mood linkage and subjective performance in professional sport teams. Journal of Applied Psychology, 85, 848–859.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".