Youth Athletes’ Moral Decision Making Levels and Sportspersonship Behavior Levels of Coaches
Bibliographic record
Abstract
The aim of this study is to determine moral decision making attitudes of youth athletes and sportspersonship behavior levels of their coaches; and to test them in terms of certain variables. The study is in a descriptive research model; the sample group consists of 294 certified athletes playing in youth sports teams; the ages range between 11 and 17. As measurement, “Attitudes to Moral Decision-Making in Youth Sport Questionnaire-2 (AMDYSQ-2)” and “Sportspersonship Coaching Behaviors Scale (SCBS)” were used. In order to test whether the data meet the prerequisite for multivariable tests, Skewness and Kurtosis values and Levene and Box M values were investigated. In the analysis of data, descriptive statistics methods, MANOVA, ANOVA and Pearson Correlation analysis were used. In order to determine the reliability of scales, their Cronbach Alpha internal consistency coefficients were calculated. MANOVA results show that AMDYSQ-2 has a significant impact on all independent variables whereas SCBS has a significant impact on “gender” and “branch involvement duration” variables. According to the correlation results, there is a positive and low-level significant correlation between “acceptance of cheating” and “keep winning in proportion” factors of SCBS and “setting expectations for good sportspersonship”, “teaching good sportspersonship”, “reinforcing sportspersonship”, “prioritizing winning over good sportspersonship” and “modeling good sportspersonship” factors of AMDYSQ-2. As a result, it is concluded that for the athletes, “acceptance of cheating” factor of AMDYSQ-2 and “setting expectations for good sportspersonship” factor of SCBS are the most important factors.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".