The relationship of diverse leisure activities with flourishing
Bibliographic record
Abstract
that encompasses subjective, psychological, and social wellbeing and is linked with physical health and functioning. However, little research has been done to show how participation in various forms of leisure might be associated with this flourishing typology. Drawing on data from community data with over 5,000 adult participants, we assessed how leisure is associated with a flourishing typology. For the present analyses, we focus on scales that assessed social leisure (e.g., socializing with friends), cultural leisure (e.g., festival attendance), home-based leisure (e.g., reading books for pleasure), physically active leisure (e.g., moderate or vigorous), and media-based leisure (e.g., time spent playing computer games or watching TV). A flourishing typology was constructed from single-item ratings on life satisfaction (subjective wellbeing), psychological well-being (self-perceptions that one's life activities are worthwhile), and social wellbeing (sense of belonging). Flourishing was linked to greater participation in cultural, social, home-based, and physically active leisure. Greater time spent playing computer games and watching TV was associated with languishing. Thus, certain forms of leisure reflect flourishing and others are linked with languishing. The nature of these associations remains to be explored, in particular, whether leisure contributes to flourishing or if flourishing facilitates certain forms of leisure participation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".