Risk Taking in Avalanche Terrain: A Study of the Human Factor Contribution
Bibliographic record
Abstract
OBJECTIVE: To discover possible associations between the human factors and avalanche incidents. DESIGN: Self-report, intercept, and Web-based, 1-year retrospective, cross-sectional study. SETTING: Mountain Equipment Co-op stores in Calgary and Vancouver, Canada. PARTICIPANTS: People shopping at the store and who had entered avalanche terrain in the past 12 months were invited to complete the survey (n = 447). INDEPENDENT VARIABLES: Sex, age, sport activity, days of exposure, years of experience, socioeconomic status, level of training, risk propensity, and motivation. MAIN OUTCOME MEASURES: Experiencing an avalanche incident. RESULTS: Women and those traveling with women were less likely to experience an avalanche incident [odds ratio (OR) = 0.45; 95% confidence interval (CI), 0.21-0.96]. Those with the most training were more likely to report experiencing an avalanche incident (OR = 6.86; 95% CI, 2.37-19.83), but this difference was attenuated (OR = 2.25) and not statistically significant (95% CI, 0.57-8.81) after adjustment for exposure. Experience was not found to be a factor. Being motivated to seek intense experiences was found to be a factor (OR = 2.19; 95% CI, 1.03-4.66), whereas being motivated to create memorable experiences was protective (OR = 0.29; 95% CI, 0.10-0.86). CONCLUSIONS: The results of this study suggest that people exposing themselves to avalanche risk do so to satisfy inherited and learned motivational needs and that some motivations are associated with higher or lower risk taking than others. Training appears to be exploited so as to increase access to these benefits rather than reduce risk. Within this risk/reward paradigm, risk taking among men is moderated by the presence of a woman in the group.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".