Advancing ski tourism transformations to climate change: A multi-stakeholder participatory approach in diverse Canadian destinations
Bibliographic record
Abstract
Canadian ski tourism destinations face increasing climate and carbon risks yet are not currently prepared to adapt to climate change or a decarbonized future. Considering the urgency of climate change and complexity of tourism systems, ski destinations need research identifying stakeholder-held climate and carbon risk perceptions, wider socioeconomic determinants of climate preparedness, and opportunities to accelerate climate decision-making and responsiveness. Using socioeconomic system frameworks, this study analyses secondary research including academic literature, climate action plans, alongside primary qualitative research collected from industry, government and community stakeholder narratives to investigate climate change and climate responsiveness in five Canadian ski tourism destinations. Despite localized climate and carbon risks, results highlight patterns impeding climate preparedness including rapid tourism growth, recreation resource corporatization, externalized climate action and sustainability, inequities, and lack of aspirational collective visioning. Conversely, stakeholders' pluralistic tourism and recreation values, sense-of-place, and interdependent relationships reveal pathways for mountain tourism destinations to transform towards climate resilient, sustainable, and just futures.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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 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".