Examination of Youth Team Athletes' Social Values According to Some Variables
Bibliographic record
Abstract
In this study, it was aimed to examine of youth team athletes' social values according to some variables. The study was carried out by screening model and includes in range of 9-17 years 273 youth team athletes who take part in individual and team sports such as Taekwondo, Handball, Badminton, Wrestling, Volleyball and Football."A tool for Measuring Values: Multi-Dimensional Social Values Scale" developed by Bolat (2013) and "Demographic Characteristic Questionnaire" were used.For statistical analysis of the data obtained from the study, arithmetic mean and standard deviation were applied. Since the variable did not meet the normal distribution and homogeneity conditions, t-test and ANOVA test were applied from the parametric tests and significance level of .05 was selected for statistical significance.As a result of the study, according to the age variable, statistically significant differences were found in the Family Values, Scientific Values, Working-Job Values, Religious Values, Traditional Values and Political Values sub-dimensions of 11-12 age group athletes. There was a significant difference in Scientific Values, Working-Job Values, Religious Values and Traditional Values sub-dimension scores of the athletes according to gender variable. It was also found out that team athletes' scores of Family Values, Scientific Values, Religious Values and Traditional Values sub-dimension were higher than individual athletes.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".