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Record W3188971340 · doi:10.1080/11745398.2021.1949737

Effects of leisure constraints and negotiation on activity enjoyment: a forgotten part of the leisure constraints theory

2021· article· en· W3188971340 on OpenAlexaffabout
Shintaro Kono, Eiji Ito

Bibliographic record

VenueAnnals of Leisure Research · 2021
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Alberta
FundersJapan Society for the Promotion of Science
KeywordsNegotiationConstraint (computer-aided design)Context (archaeology)Outcome (game theory)PsychologyLeisure activitySocial psychologyRegression analysisSociologyEconomicsComputer scienceMicroeconomicsGeographyMathematics

Abstract

fetched live from OpenAlex

Although identified in the definition of leisure constraints, leisure enjoyment has been rarely studied as an outcome of constraints and constraint negotiation. The purpose of this paper is, therefore, to examine the associations among leisure constraints, constraint negotiation, and enjoyment, within the context of leisure-time physical activity (LTPA). Cross-sectional online survey data from 618 Japanese and Euro-Canadian adults were used. Regression results suggested that across different levels of LTPA, enjoyment was negatively associated with constraints and positively with constraint negotiation. Follow-up regression analyses at sub-category level identified specific types of leisure constraints and negotiation strategies particularly pertinent to enjoyment. We conclude that leisure enjoyment is a direct outcome of constraints and constraint negotiation, which supports the call to extend the leisure constraints theory beyond participation as the outcome. Moreover, we suggest that facilitating leisure enjoyment requires awareness of different types of constraints and negotiation strategies depending on activity contexts.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.110
GPT teacher head0.420
Teacher spread0.309 · 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.

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

Citations32
Published2021
Admission routes2
Has abstractyes

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