Exploring Potential Visitor Response to Climate-Induced Environmental Changes in Canada's Rocky Mountain National Parks
Bibliographic record
Abstract
The scientific community and park professionals recognize that climate change could have a substantial impact on the natural landscape of mountain parks worldwide, with important implications for conservation policy and park planning. Little is known however about how tourists may respond to these projected environmental changes. To explore this question in the context of Canada's Rocky Mountain national parks, a visitor survey was administered (n = 809) in two national parks: Banff and Waterton Lakes. The environmental change scenarios constructed for the early and mid-decades of the 21st century were found to have minimal influence on intention to visit. The environmental change scenario for the latter decades, under a high emission climate change scenario, was found to have a negative effect on intention to visit, as 36% of respondents indicated they would visit the parks less often and 25% not at all. Visitors most likely to be negatively affected by climate-induced environmental change were nature-based tourists from overseas, motivated by the opportunity to view mountain landscapes and wildlife. The hitherto largely overlooked conceptual and methodological challenges of understanding how tourists may respond to multidecadal environmental changes induced by global climate change in any tourism setting is also discussed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".