Effortful leisure is a source of meaning in everyday life
Bibliographic record
Abstract
People derive much purpose from their work, yet time spent on work is decreasing. Here, we ask if effortful leisure is a powerful source of meaning and purpose that could supplement the reduction in labor time. In five studies (N = 2569), we investigated the relationship between effort and meaning in leisure activities. In Study 1 (N = 1145), we found that participants rated effortful activities as more meaningful, although less enjoyable, suggesting a trade-off between eudaimonic and hedonic wellbeing. Studies 2a (N = 375), 2b (N = 389), and 3 (N = 400) provided causal evidence by comparing effortful (Sudoku puzzling) and non-effortful leisure (watching videos in Studies 2a and 2b; Click-to-Reveal game in Study 3). Effortful activities consistently felt more meaningful, though the effects plateaued at higher levels of effort. Finally, Study 4 (N = 260) used experience sampling to assess activities as they occurred in real life. Effortful leisure fostered meaning while maintaining enjoyment, whereas other activities tended to feel less enjoyable with increased effort. Across all studies, we found that effort promotes meaningful experiences, particularly in leisure contexts, where effort does not diminish enjoyment. Effortful leisure may offer a powerful opportunity to supplement or replace the once plentiful purpose we derived from our now diminishing time at work.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".