Bibliographic record
Abstract
The aim of this study is to determine the leisure satisfaction levels of students who study sports sciences. In addition, in the study, the satisfaction levels of the students were compared according to their gender, departments and the most preferred activity type in leisure. The research was in quantitative descriptive design and consisted of a total of 379 sports sciences students, including 144 female and 235 men. The “Leisure Satisfaction Scale” developed by Beard and Raghep (1992) which is adapted to Turkish by Gökçe and Orhan (2011) and the “Personal Information Form” developed by the researcher were used as data collection tools. T-test, ANOVA and one-way MANOVA test techniques were used in the analysis of the data. According to the research findings, the leisure satisfaction of the students of Sports Sciences has been observed to be high level. In general, it was found that leisure satisfaction of those who do most physical activity is higher than those who did social, intellectual and artistic etc. activity. According to the gender factor, leisure satisfaction total score did not make a difference, but it was seen that female had higher scores than psychological and relaxation dimensions. In the comparisons between the departments, it was determined that the recreation department had more leisure satisfaction than the coaching and physical education teaching department students. It has been observed that the highest score section is recreation and the lowest score section is coaching. Finally, the research findings were discussed.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".