research Supporting Sound Decisions: A Professional Perspective on Recreational Avalanche Accident Prevention
Bibliographic record
Abstract
Relative to recreationists, avalanche professionals in Canada have a high success rate for managing avalanche hazard and making sound decisions in avalanche terrain. This success invites the question: What can be learned from these successes relative to avalanche education, decision support and accident prevention for backcountry recreationists? I surveyed Canadian avalanche professionals using a mail-in questionnaire on core knowledge and skills for sound avalanche decision making, key areas of education that can improve avalanche decision making, effective methods to communicate avalanche hazard, and the potential of a recreational decision support framework to improve decisionmaking and result in fewer recreational avalanche accidents and fatalities. The avalanche practitioners in this study identified human factors and choice of terrain as the primary causes of recreational avalanche accidents and recommended that recreational education targeted in these two areas would effectively reduce avalanche accidents. Three meta-themes emerged to support sound decisions by recreationists: training and education, hazard communication and decision support. In this paper, I examine the results of this survey within the context of theories of adult learning and decision science. I offer an analysis of why it is important to look at avalanche accident prevention from a human sciences research perspective and propose a systemic approach to supporting sound recreational decision-making. Based upon these survey results, I advocate strong support for the implementation of a recreational decision support framework in Canada, although there were several complexities identified by survey respondents. It is clear that the integration of expertise from a wide range of disciplines will be required to design and implement an effective and integrated framework that will support sound decisions and reduce the number of avalanche accidents and fatalities in Canada.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".