Leisure Facilitators and Recreation Specialization for Female Participants in Running Events
Bibliographic record
Abstract
The purpose of this study was to identify the relationship between leisure facilitators and recreation specialization for female participants in running events. To achieve the goal of this study, 330 surveys were collected from female participants living in the Seoul area selected through a convenience sampling method. After examining the correlation between leisure facilitators and recreation specialization, the relationship between the two variables was assessed through multiple linear regression analysis. First, for socio-demographic variables pertaining to females who participated in running events, significant differences were found between frequency of participating in running events, participants’ running duration, running exercise participation time, and frequency of running exercise participation as sub-factors of leisure facilitators. Second, for socio-demographic variables pertaining to females who participated in running events, significant differences were revealed between income, frequency of participating in running events, participants’ running duration, running exercise participation time, and frequency of running exercise participation as sub-factors of recreation specialization. Last, regarding sub-factors of leisure facilitators for females participating in running events, intrapersonal constraints, interpersonal constraints, and structural constraints had positive effects on past experience as an economic investment in recreation specialization. Intrapersonal constraints and structural constraints had positive effects on centrality-to-lifestyle for recreation specialization factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".