Bibliographic record
Abstract
The criteria individuals use and much of the decision making related knowledge base in professional, mountain guiding has remained poorly understood even to active practicing professionals. Many mountain guides might have difficulty in expressing exactly how field-based risk management decisions are made in particular, how intuition is used. The increased interest in decision-making is not unique to mountain guiding and the avalanche industry. There is much to be learned from how other fields have approached the challenge of understanding the complexities of the decision making process. Research that helps to describe the innovative practices and extant knowledge of mountain guiding will help theory and practice to be more in harmony. With an annual average fatality rate over the last ten years of just under two and a half fatalities per 100,000 skier days in the Canadian mechanized ski industry, it is not unreasonable to suggest that there is considerable knowledge entrenched within the daily activities of the practitioners (BC Coroner, 2003; Israelson, 2008). However it is arguable that even this number of fatalities is too many and all efforts should be made to reduce the number of fatalities in guided groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".