Relationships among self-construal, control, and positive affect in Japanese undergraduate students’ leisure experience
Bibliographic record
Abstract
Previous studies have indicated that control and positive affect, which are known to be important aspects of leisure experience, contribute to our psychological well-being. Recently, Ito and Walker’s (2016. Cultural commonality and specificity in Japanese and Euro-Canadian undergraduate students’ leisure experiences: An exploratory study on control and positive affect. Leisure Sciences, 38, 249–267) experience sampling method (ESM) study identified that Japanese undergraduate students experienced primary control, the adjustment aspect of secondary control, and high- and low-arousal positive affect more, and the acceptance aspect of secondary control less, during leisure versus non-leisure participation. This follow-up ESM study, conducted with 41 Japanese undergraduate students, furthers their work by examining the roles of self-construal and types of leisure activities in regard to the significant relationships between leisure participation and experience. Hierarchical linear modelling results indicated that: (a) the moderator effects of self-construal did not exist between leisure participation and experience, and (b) the relationship between leisure participation and experience did not largely vary across types of leisure activities. This study contributes to the growing, yet still understudied, research theme of non-Western leisure and self-construal.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".