The Role of Leisure Management in Study-Leisure Conflict in Secondary Education
Bibliographic record
Abstract
In the study where the relationship between study-leisure conflict and leisure management and the effect of leisure management on this conflict was investigated, it was also determined how certain variables changed the level of SLCS and LM. The study included 236 students studying in total 4 secondary and high schools in Ankara, and the data were obtained through personal information form as well as study-conflict and leisure management scales. Study and leisure conflict scale; “Study-Leisure Conflict Scale” is a measurement tool developed by Işık and Demirel (2018), inspired by the scale of “Measurement of Work-Leisure” (Tsaur et al., 2012), consisting of 20 questions and 5 sub-dimensions. “Leisure Management” scale is a measurement tool developed by Wang et al. (2011), consisting of 15 questions and 4 sub-dimensions and adapted to Turkish by Akgül and Karaküçük (2015). Parametric tests were applied since it was determined that the data showed normal distribution. In the analysis of the data, descriptive statistics, independent sample T test, one-way analysis of variance ANOVA test and Tukey (HSD-LSD) test were used for intra-group comparisons, and Pearson Correlation test and Regression analysis were used to determine the relationship and effect. The findings of the research show that the participants displayed a level of conflict below average; on the other hand, they showed a level of leisure management slightly above the average. Partial weak relations were determined between the study-leisure conflict scale and the leisure-time management scale, while at the same time; it decreased the level of leisure-study conflict of leisure time, although it was not significant. It is among other findings that certain variables change the measurement tools. As a result of the research, it is thought that the partial weak relations between study-leisure conflict and leisure management will return to the expected negative momentum by the students having effective knowledge and skills about leisure management. Accordingly, it is revealed by the findings of the study that more information on leisure time, management and conflict resolution should be transferred in education programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".