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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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 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".