Unravelling the Canadian ski market decline: exploring constraints and facilitators affecting participation across different skier groups
Bibliographic record
Abstract
Purpose/Rationale Recent evidence shows declining downhill skiing participation. Finding ways to counteract this decline is necessary to avoid further effects on the ski industry.Design/Methodology/Approach An online survey was created (29 items) to test the leisure constraints model; (1) skiing participation behaviour (four items), (2) constraints (12 items), (3) facilitators (seven items), and (4) demographics (six items).Findings Intrapersonal constraints significantly predicted ski behaviour with a negative effect (β = −3.21, SE = 0.95, C.R. = −3.37, p < .001). Structural constraints significantly predicted ski behaviour (β = 3.61, SE = 1.50, C.R. = 2.40, p = .016), showing a strong positive relationship between structural constraints and ski behaviour. Facilitators were a significant positive predictor of ski behaviour (β = 0.17, SE = 0.02, C.R. = 8.23, p < .001). Demographics significantly predicted ski behaviour (β = .83, SE = 0.16, C.R. = 5.29, p < .001).Practical implications Segmenting the market into different groups makes it possible to predict ski consumer behaviour more effectively.Research contribution The findings of this study corroborate prior research and indicate that intrapersonal constraints, facilitators, and demographics are significant predictors of ski participation behaviour.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".