The quadripartite approach to passion in sport: a prospective and cross-domain analysis with intercollegiate student-athletes
Bibliographic record
Abstract
We tested if distinct combinations of harmonious passion and obsessive passion for sport were associated with outcomes within sport, academics, and in one’s life. We analysed data from the Student-athlete Well-being and Achievement Project (SWAP), a study in which intercollegiate student-athletes (N = 298) completed assessments of harmonious and obsessive passion at the start of a season, and assessments of performance, experiences, and satisfaction in sport, academics, and in life at the end of a season. Results showed that high harmonious passion combined with low obsessive passion (i.e., pure harmonious passion) was most often associated with the most adaptive outcomes, whereas high obsessive passion combined with low harmonious passion (i.e., pure obsessive passion) was associated with the least adaptive outcomes. These results build on previous research with recreational athletes [Schellenberg, B. J. I., Verner-Filion, J., Gaudreau, P., & Mbabaali, S. 2021. The two dimensions of passion for sport: A new look using a quadripartite approach. Journal of Sport & Exercise Psychology, 43(6), 459–476. https://doi.org/10.1123/jsep.2021-0048] by showing the benefits of pursuing competitive sport with high harmonious passion, especially pure harmonious passion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".