Predicting moral behaviour in sport: individual and interactive relationships involving motivational climate, gender, and perfectionism
Bibliographic record
Abstract
A large proportion of Canadian youth aged 15 to 19 ? approximately 60 percent ? participate in organized sport (Canadian Fitness & Lifestyle Research Institute, 2015). Given this significant number, popular media portrayals of the behaviour of athletes have the potential to shape the behaviour of youth athletes (Bush, Martin, & Bush, 2004). Appropriate behaviour is determined in part by the internalized morals and values one has adopted (Bandura, 1991). With the exception of family, sports provide one of the most influential social environments with which an individual may be involved (Bruner, Boardley, & C?t?, 2014). Throughout the course of a competition, incidents may occur that result in athletes choosing to behave in a manner that results in positive outcomes for others, or choosing to behave in a way that results in negative outcomes for others. For example, Sarah Tucholsky was a senior outfielder for the University of Western Oregon?s softball team when she hit her first career home run (CBS News, 2008). In her excitement, she missed first base, and when she turned back to tag the base, she injured her knee. Unable to run, the opposing team?s first base woman and their shortstop asked permission and carried Tucholsky around the bases resulting in a three-run victory for Tucholsky?s team. In contrast, Elizabeth Lambert was a defender with the University of New Mexico?s women?s soccer team when she became infamous following a game in which she exhibited several aggressive and decidedly unsportspersonlike behaviours. Throughout the game she punched, tripped, tackled, and finally pulled an opposing player to the ground by her ponytail (Clayton, 2010), behaviours that eventually earned her a yellow card (a penalty in soccer). \nIncidents of positive and negative behaviour in sport, as exemplified by the experiences of Sarah Tucholsky and Elizabeth Lambert described earlier, have received considerable attention from both the public and the media highlighting the value of investigating moral behaviour in sport (Perry, Clough, Crust, Nabb, & Nicholls, 2015). How athletes choose between positive and negative behaviour may be influenced by many factors, including the influence of their personality and the influence of the environment. An even greater understanding of moral behaviour in sport may be obtained by simultaneously considering the interaction between personality and environmental factors (Bandura, 1991; Hodge & Lonsdale, 2011; Kavussanu, 2012). The present study was conducted in line with this contention.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".