Climate Change Impact on Outdoor Organizations Today
Bibliographic record
Abstract
INTRODUCTION: Outdoor recreation and tourism providers, education programs, and outdoor recreation facilities are experiencing the effects of climate change and severe weather firsthand. This research assessed the impact that climate change was having on these operations in 2023. METHODS: Respondents from 127 outdoor organizations completed an online survey assessing the impacts of climate change and severe weather. Any outdoor operation that owed a duty of care to clients who they take outdoors or host at their facilities was invited to participate. This included outdoor tourism and recreation providers, outdoor education programs (both school and expedition based), groups involved in conservation work, and facilities such as parks, ski areas, and other outdoor recreation facilities. Respondents were from Canada's far north to Mexico, with 14% from further international locations. RESULTS: Climate change is having moderate to serious impacts on outdoor operations. Extreme heat and air quality were of primary concern, with storm event flooding, wildfires, snowpack, and changes in the ranges of disease-carrying insects top concerns. Fewer than half the operations have established criteria to aid in decision making, yet most had to revise operational plans in 2023 due to extreme weather. CONCLUSIONS: There was pervasive uncertainty regarding decisions involving extreme heat and air quality, particularly the short-term health impacts on clients and the long-term health impacts on workers. There was uncertainty regarding trusted sources for guidance and the many overlapping or contradictory jurisdictional recommendations. Practical direction is required for operations and decision makers, as is further research specific to this sector's needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".