How do passionate athletes "cope" with positive events? Relationships between passion, savouring, and dampening
Bibliographic record
Abstract
Good things happen in sport; athletes win games, earn awards, reach milestones, and experience many other types of positive events. Sometimes athletes respond to these positive events by engaging in savouring and attempt to maintain, enhance, or prolong their positive emotions. But athletes can also respond to positive events by engaging in dampening and attempt to stifle and decrease their positive feelings. Our aim in this research was to build on recent findings with students and sports fans (Schellenberg & Gaudreau, in press), and test if responses to positive events were predicted by the extent to which athletes' passion for sport was harmonious and obsessive (Vallerand, 2015). Athletes (N = 420) recruited from a crowdsourcing website (Prolific Academic) completed an online survey in which they reported their levels of harmonious and obsessive passion for their sport and the extent to which they would engage in savouring and dampening in response to a positive event in their sport. Using partial correlations that controlled for both the positivity of the event and the other passion type, we found that savouring was positively predicted by both harmonious and obsessive passion, but that dampening was negatively predicted by harmonious passion and positively predicted by obsessive passion. These findings replicate past research with other populations by showing that they ways in which athletes cope with positive events is predicted by the extent to which their passion for sport is harmonious and obsessive.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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".