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Record W4366212151 · doi:10.3389/fpsyg.2023.1130906

The relationship of diverse leisure activities with flourishing

2023· article· en· W4366212151 on OpenAlexaff
Steven E. Mock, Bryan Smale

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFlourishingTypologyPsychologyPleasureSocial psychologyLeisure satisfactionLife satisfactionLeisure studiesWell-beingDevelopmental psychologyRecreationSociologyPolitical science

Abstract

fetched live from OpenAlex

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.335
Teacher spread0.295 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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