Gaining insight from the most challenging expedition: climate change from the perspective of Canadian mountain guides
Bibliographic record
Abstract
Nature based tourism (NBT) is becoming increasingly popular, particularly following the COVID-19 pandemic as people began to sought outdoor activities. Accompanying the projected rise in NBT demand in a post COVID-19 era are increasing challenges associated with climate change, particularly in mountain regions. However, there is limited local knowledge documented to date from those who are intricately involved in mountain NBT activities and have experienced the impacts of climate change first hand. Using an online survey (n = 169), this research is the first to present the intimate knowledge of mountain guides in Canada, offering novel insight into climate change risks and opportunities for NBT in mountain regions, including strategies to contend with risk and adaptation. From this survey, 99% of guides indicated that they have experienced change in the mountain environment throughout the course of their career and due to the adaptive nature of guides, many have already implemented strategies to adapt to the impacts of climate change. While findings presented in this paper offer practical knowledge to plan for a future threatened with rapid climatic change, further research is required to explore effectiveness of adaptation strategies, scope of adaptive capacity, changes in natural infrastructure, and guides’ roles as educators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".