Understanding leisure’s impacts on sources of life worth living: a multi-domain approach
Bibliographic record
Abstract
Although leisure’s relationships with well-being have been widely studied, the literature lacks non-Western and eudaimonic perspectives. Moreover, leisure researchers have often focused exclusively on leisure. This leaves leisure’s impacts on well-being compared to other life domains understudied. The purpose of this study is to identify various sources of ikigai or ‘life worth living’ in Japanese and to explore leisure’s influences on these sources. The secondary, thematic analysis was applied to data from 27 photo-elicitation interviews with Japanese university students. Five main themes were identified. Self referred to personal standards with which students evaluated the value of their activities and relationships. Tanoshimi, or enjoyment, provided present-focused experiences, positive short-term goals and rewards, and elements of novelty. Shigoto, or work, gave students roles, goals and motivations, and a sense of growth and achievement. Self-care replenished physical, mental, and social resources to continue tanoshimi and shigoto. In authentic relationships, students shared valuable activities with their significant others and exchanged support. Leisure allowed for self-expression, while most tanoshimi activities were deemed as leisure. Some shigoto activities were serious leisure. Leisure activities were also used to do self-care and to maintain authentic relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".