Explaining sport-based moral behaviour among adolescent athletes: The interactive roles of perfectionism and gender
Bibliographic record
Abstract
The purpose of this study was to examine the degree to which adolescent athletes' moral behaviour in sport is dependent on their perfectionistic orientation and their gender. Female (n = 123) and male (n = 132) adolescent club basketball players (Mage = 17.44 years, SD = 1.03) completed the Sport Multidimensional Perfectionism Scale-2 (Sport-MPS-2: Gotwals & Dunn, 2009) and the Prosocial and Antisocial Behaviour in Sport Scale (PABSS; Kavussanu & Boardley, 2009). Cluster analysis conducted upon the participants' Sport-MPS-2 subscale scores revealed that the sample could be categorized into three theoretically meaningful groups: Pure Personal Standards Perfectionists, Mixed Perfectionists, and Non-Perfectionists (see Gaudreau & Thompson, 2010). A 2 (gender) × 3 (perfectionism group) factorial MANOVA then tested for main and interaction effects across the four PABSS subscales. Regarding prosocial behaviour towards opponents, mixed perfectionists and pure personal standards perfectionists showed higher levels than non-perfectionists and females showed higher levels than males. Regarding prosocial behaviour towards teammates, the same pattern of differences emerged across the perfectionism subtypes, but only among females. Regarding antisocial behaviour towards opponents and antisocial behavior towards teammates, the mixed perfectionists showed higher levels than the pure personal standards perfectionists and the non-perfectionists, but only among males. In discussion we relate the present cluster solution to perfectionism research and theory; speculate why mixed perfectionists and pure personal standards perfectionists would report different levels of antisocial behaviour, but similar levels of prosocial behaviour; and explore why these differences would be exhibited among one gender, but not another.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".