Empirical investigation of the relationship between serious leisure and meaning in life among Japanese and Euro-Canadians
Bibliographic record
Abstract
Serious leisure (SL) is a specific leisure experience characterised by perseverance, leisure career, personal effort, durable benefits, unique ethos, and identification with the activity. As it results in self-actualisation and self-expression, Robert Stebbins has proposed that SL does not only increase participants’ hedonic well-being (e.g. pleasant feelings), but also enhance their eudaimonic well-being [e.g. meaning in life (MIL), self-expressiveness, virtue]. Although this argument makes logical sense, it has not been empirically tested. The purpose of this research is to empirically examine the relationship between SL and eudaimonic well-being focusing on MIL. We used data from 207 Japanese and 202 Euro-Canadian middle-aged and older adults collected through a cross-sectional online survey. After multi-group confirmatory factor analysis, multiple mediation analyses were conducted to test whether SL core characteristics impacted MIL or its sub-dimensions (i.e. purpose, coherence, and significance) both directly and indirectly via personal and interpersonal rewards of SL. Results suggested that among Japanese, SL was positively related to MIL both directly and indirectly via SL’s personal rewards. Among Euro-Canadians, the direct link was limited to only a few MIL sub-dimensions, and indirect effects were not significant. These mixed results were discussed in relation to SL, eudaimonic well-being, and culture.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".