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Supporting Sound Decisions: A Professional Perspective on Recreational Avalanche Accident Prevention in Canada

2004· article· en· W18454197 on OpenAlexaboutno aff
Laura E. Adams

Bibliographic record

VenueProceedings of the 2004 International Snow Science Workshop, Jackson Hole, Wyoming · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersNational Cancer InstituteWellcome Trust
KeywordsRecreationContext (archaeology)HazardPsychologyApplied psychologyGeographyPolitical science

Abstract

fetched live from OpenAlex

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. Respondents 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.096
Threshold uncertainty score0.698

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0200.007
Scholarly communication0.0070.002
Open science0.0030.004
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0260.001

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.

Opus teacher head0.013
GPT teacher head0.298
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2004
Admission routes1
Has abstractyes

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Same venueProceedings of the 2004 International Snow Science Workshop, Jackson Hole, WyomingSame topicLandslides and related hazardsFrench-language works237,207