Passion and psychological commitment in competitive collegiate sport
Bibliographic record
Abstract
Introduction Enduring participation in sport is partly driven by psychological commitment. Athletes' preferences and attitudes are important to predicting their participation (Ajzen, 1991). Drawing from the literature regarding passion and goal-directed behaviours (Vallerand & Miquelon, 2007) and passion and involvement (Wilson & Potwarka, 2014), this study explores the connection between passion and collegiate athletes' psychological commitment to sport. Method and Results Collegiate athletes participating in team sports at four Canadian universities (n = 587) completed measures of passion (Vallerand et al., 2003) and psychological commitment to their sport (Kyle et al., 2007). Cluster analysis produced four equally-sized passion profiles comprised of combinations of high and low harmonious and obsessive passion. A one-way ANOVA suggested that commitment to sport differed between the segments, F (3, 574) = 52.14, p < .001. Commitment was highest when both types of passion were high (M = 5.75) and lowest when both types of passion were low (M = 3.07). However, those with high levels of harmonious passion and low levels of obsessive passion (M = 4.57) were no more committed to participation than those with low levels of harmonious passion and high levels of obsessive passion (M = 4.67). Discussion Both harmonious and obsessive passion play important roles in collegiate athletes' commitment to sport. This finding challenges previous research that has shed a negative light on obsessive passion (Wilson & Potwarka, 2015). Participatory attitudes such as commitment are linked with positive and negative aspects of passion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".