Leisure’s Relationships with Hedonic and Eudaimonic Well-Being in Daily Life: An Experience Sampling Approach
Bibliographic record
Abstract
Research on leisure and subjective well-being has focused on hedonic well-being (e.g., positive affect). Leisure’s relationships with eudaimonic well-being (e.g., meaning) remains underexplored. The literature also lacks non-Western perspectives. This study examined leisure’s relations with shiawase and ikigai, Japanese concepts that represent hedonic and eudaimonic well-being, respectively. A smartphone-based experience sampling method was used. A total of 2,207 responses were collected from 83 Japanese university students. Multilevel linear modeling showed that free time (e.g., lunch, evenings) predicted higher levels of daily shiawase and ikigai, while ikigai appeared to stay higher during afternoon. Various leisure activities positively predicted shiawase and ikigai levels, with event/trip, eating/drinking, socializing, and hobbies being the best predictors. A few activities (e.g., exercise) differentially predicted the outcomes. Among subjective experiences common during leisure, intrinsic motivation, enjoyment, stimulation, and comfort were positively correlated to shiawase and ikigai, whereas effort predicted only ikigai.
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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 | 0.003 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".