Leisure Constraints, Leisure Constraints Negotiation and Recreation Specialization for Water-Based Tourism Participants in Busan
Bibliographic record
Abstract
The purpose of this study was to identify the relationships among leisure constraints, leisure constraints negotiation, and recreation specialization for water-based tourism participants in Busan. Through this study, coastal cities of Korea (e.g., Busan) may attempt to develop marine leisure infrastructure. To achieve the goal of this study, 339 surveys were collected from male and female adults who planned to participate in water-based tourism event in 2017 were delineated as the study population. A convenient, non-random sampling method was used to select participants. After examining the correlation among leisure constraints, leisure constraints negotiation and recreation specialization, the relationships among the three variables was assessed through multiple linear regression analysis. The results of this study were as follows. First, regarding sub-factors of leisure constraints for water-based tourism participants, intrapersonal constraints, interpersonal constraints, and structural constraints had negative effects on leisure constraints negotiation. Second, the sub-factors of intrapersonal constraints and structural constraints had negative effect on recreation specialization, and interpersonal constraints were not statistically significant. Third, leisure constraints negotiation had a partially positive effect on recreation specialization.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".