How do passionate sports fans respond when good things happen? A look at savouring and dampening
Bibliographic record
Abstract
Passionate sports fans often experience positive events such as team victories and accomplishments. But how do fans respond when these events happen? In two studies, we tested if harmonious and obsessive varieties of passion predicted the extent to which passionate fans engage in savouring by attempting to maintain, enhance, or prolong their positive emotions following positive events, and engage in dampening by attempting to decrease their positive feelings. In Study 1, undergraduate sports fans (n = 321) reported levels of harmonious and obsessive passion for their favourite team, the extent to which they generally savour positive events while supporting their team, and how they responded to a recent team victory by savouring and dampening. In Study 2, soccer fans (n = 394) recruited from a crowdsourcing website (Prolific Academic) participated in an experimental study that tested if the relationships between passion types, savouring, and dampening depended on whether a positive event was the result of an in-progress (semi-final victory) or completed (final victory) achievement. In both studies, harmonious passion predicted greater savouring, whereas obsessive passion predicted less savouring and greater dampening. Goal status moderated the relationships between both passion types and dampening: high levels of either harmonious or obsessive passion predicted less dampening in the completed compared to the in-progress condition. These results reveal that passion types matter for predicting who tries to make themselves feel better, or worse, following positive events, and have implications for our understanding of the pathways that link passion types with outcomes in sports fans.Acknowledgments: Social Sciences and Humanities Research Council of Canada
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".