Losing passion: A test of the seasonal attenuation of passion (SAP) hypothesis across three longitudinal studies with athletes and sport fans
Bibliographic record
Abstract
People often feel passionate toward activities in sport. But passion can change, and we know very little about how or when passion for sport changes over time. Here we present a hypothesis about how, when, and why passion changes over time – the seasonal attenuation of passion (SAP) hypothesis – which predicts that levels of passion toward activities will tend to decline over the course of a season in sport. We tested this hypothesis in three studies with intercollegiate volleyball players (N = 421), intercollegiate athletes from various sports (N = 298), and fans of the Winnipeg Jets (N = 418). In each study, participants reported levels of passion (i.e., harmonious passion, obsessive passion, general passion) at either the start and end of a season (Study 1) or at the start, middle, and end of a season (Studies 2 and 3). Using latent change score modeling (Study 1) and latent growth modeling (Studies 2 and 3), we found that all scores of passion decreased over the course of each season. This has implications for our understanding of how passion changes over time, especially in sporting activities which are often organized in recurring seasons; there appears to be a tendency for a season to sap people’s passion over time.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".