Exploring the gendered landscape of the avalanche safety industry – barriers, benefits and potential drawbacks of professional diversity
Bibliographic record
Abstract
Snow and avalanche safety is a male dominated field. The aim of this paper is to increase the knowledge on the gendered conditions and the prerequisites this poses for snow and avalanche safety professionals, and to shed light on why relatively few women enter and stay in the industry. Our analysis is based on quantitative and qualitative data from a survey sent out to avalanche professionals in North America, Continental Europe, and Scandinavia. We inductively coded and categorized responses to open-ended questions into themes based on patterns and commonalities, using a content analysis. We find that avalanche work requires a wide skill set - skills that are traditionally associated with men as well as skills that are traditionally associated with women, and that our participants think that increased diversity at large would benefit the industry. However, our data also reveal persisting cultural and structural gender barriers that make it more difficult for women and non-binary individuals to enter and thrive in the industry compared to men. We discuss management implications that can help make the industry better for all.
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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".