Savoring and dampening with passion: How passionate people respond when good things happen
Bibliographic record
Abstract
How do people react when they experience a positive event while pursuing a passionate activity? In this research, we conducted three studies to test if the extent to which people respond to positive events by engaging in savoring (i.e., attempting to maintain or enhance positive emotions) and dampening (i.e., attempting to down-regulated or stifle positive emotions) is predicted by levels of harmonious and obsessive passion for an activity. Study 1 (n = 321) and Study 2 (n = 547) both showed that harmonious passion positively predicted savoring, whereas obsessive passion predicted less savoring and greater dampening. Moreover, in Study 2, savoring mediated the relationships between both passion varieties and well-being outcomes. In Study 3, we extended these findings and tested if these relationships depended on whether a positive event was a result of an in-progress or completed achievement. Soccer fans (n = 394) imagined how they would react if their favorite team won either the semi-final (in-progress condition) or final (completed condition) of the ongoing UEFA champions league. In both scenarios, harmonious passion was a stronger predictor of savoring than obsessive passion. Obsessive passion also showed strong relationships with dampening in both scenarios, although this relationship was attenuated in the completed condition. Overall, these results reveal that passion varieties matter for predicting how people manage their good feelings following positive events, a finding that has implications for our understanding of the pathways that link passion varieties with well-being outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".