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Record W2598952805 · doi:10.5539/ass.v13n4p117

Relationship between Leisure Facilitators and Serious Leisure among Female Korean College Soccer Participants

2017· article· en· W2598952805 on OpenAlexvenueno aff
Hee Yeob Kang, Hyung Hoon Kim, Hyun Wook Choi, Won Il Lee, Chul Won Lee

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsIntrapersonal communicationPsychologyConfirmatory factor analysisInterpersonal communicationLeisure timeLeisure activityClinical psychologySocial psychologyPhysical activityStructural equation modelingMedicinePhysical therapy

Abstract

fetched live from OpenAlex

This study identifies the relationship between leisure facilitators and serious leisure of female college soccer participants. To this end, data were collected from a total of 223 surveys from participants in female college soccer participants. The collected data were analyzed and interpreted using SPSS and AMOS program. Frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis and multiple regression analysis were performed. All tests were performed using a .05 significance level. The results of this study were as follows. First, intrapersonal facilitators of leisure facilitators had a positive effect on serious leisure. Second, interpersonal facilitators of leisure facilitators had a positive effect on serious leisure. Third, structural facilitators of leisure facilitators had a positive effect on serious leisure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.073
GPT teacher head0.373
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2017
Admission routes1
Has abstractyes

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