Motivations and Normative Evaluations of Summer Visitors at an Alpine Ski Area
Bibliographic record
Abstract
This article examines motivations of people visiting an alpine ski area in the summer season and their norms regarding acceptable and unacceptable trail conditions and densities of use at this area. Data were obtained from on-site surveys of summer visitors ( n = 422) at the Whistler Mountain ski area in British Columbia, Canada. Cluster analysis of several reasons for visiting revealed three groups ranging from a group who rated all motivation factors as most important to a group who only considered the alpine scenery as important. Norms were measured using evaluations of photographs depicting increasing trail widths and densities of sightseers/hikers and mountain bikers. Compared to the other two groups, the group who only considered the scenery important had lower normative acceptance of increasing densities of use and wider trails and had more norm crystallization or consensus about acceptable and unacceptable conditions. Research and management implications are discussed.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".