Who comes to the park and why? A cluster analysis based on serious leisure framework
Bibliographic record
Abstract
Understanding the diversity of park visitors is essential for effective planning, programming, and engagement. However, few studies segment users based on their patterns of leisure involvement and identity development. The Serious Leisure Perspective is used in this study to segment visitors to Gatineau Park, Canada according to their demographic characteristics and levels of seriousness in participation. Using two-step cluster analyses, three distinct visitor groups were identified: Casual Park Visitors, Transitional Park Participants, and Park Devotees. Further exploring the diversity within the transitional group, a secondary clustering revealed five subclusters, characterized by varying commitments, demographics, and leisure behaviours. Results demonstrate a progression in leisure involvement and how factors including gender, income, education, and activity preferences influence leisure identity development. In addition to contributing to leisure theory, the study offers practical guidance for park management, programming, and marketing through the lens of the serious leisure framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".