The Relationship Between Leisure Time Management and Perceptions of Boredom
Bibliographic record
Abstract
The purpose of this research is to examine university students’ leisure time management and perceptions of boredomaccording to various factors and to put forward the relationship between those two concepts. The test group of theresearch has been selected with purposive sampling among students from Istanbul University-Cerrahpaşa Faculty ofSport Sciences and 170 “Male” and 82 “Female” students with an average age of 21,71 ± 3,10 have volunteered totake part. In the research “Leisure Time Management Scale,” which has been developed by Wang et al. (2011) andadapted into Turkish by Akgül and Karaküçük (2015), and “Leisure Boredom Scale,” which has been developed byIso-Ahola and Weisseinger (1990), and adapted to Turkish by Kara et al. (2014), has been used. In order todetermine the personal information of participants the percentage and frequency methods; to determine whether thedata has normal distribution or not the Shapiro Wilks normalcy test has been applied and after concluding, that thedata is conformable with the parametric test conditions, MANOVA and Pearson Correlation tests have been used fordata analysis. According to the analysis; in view of gender variable, in both leisure time management and leisureboredom perceptions scale a significant difference has been observed (p<0.05). In view of age variable, in the“Programming” subdimension of leisure time management and in all subdimensions of leisure boredom perceptionscale a significant difference has been observed (p<0.05). In view of wealth variable, in the “Leisure time manner”and “Programming” subdimensions of leisure time management scale a significant difference has been observed butno difference has been observed in leisure time perception. Finally, a negative and meaningful relationship has beenobserved between the two scales. In conclusion it is possible to claim, that the leisure time management and boredomperception of participants has had significant differences in view of some variables and that when they can managetheir leisure time, they are satisfied.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".