MétaCan
Menu
Back to cohort
Record W4410794617 · doi:10.31234/osf.io/zphnm_v4

Effortful leisure is a source of meaning in everyday life.

2025· preprint· en· W4410794617 on OpenAlexfundno aff
Aidan Vern Campbell, Gregory John Depow, Srishti Agarwal, Michael Inzlicht

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial and Behavioral Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMeaning (existential)Everyday lifePsychologyAestheticsSocial psychologyArtPolitical sciencePsychotherapistLaw

Abstract

fetched live from OpenAlex

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 which could supplement the reduction in labor time. In five studies (N = 2,563), we investigated the relationship between effort and meaning in leisure activities. In Study 1, we found that participants rated effortful activities as more meaningful, although less enjoyable, suggesting a trade-off between eudaimonic and hedonic wellbeing. Studies 2a, 2b, and 3 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 used experience sampling to assess activities as they occurred in real life. Effortful leisure uniquely fosters meaning while maintaining enjoyment, whereas other activities tend to feel less enjoyable with increased effort. Across all studies, we found that effort promotes daily 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.068
GPT teacher head0.365
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicSocial and Behavioral StudiesFrench-language works237,207