Feeling safe enough: Psychological safety in the mountain guiding and avalanche profession
Bibliographic record
Abstract
This article examines perceptions of psychological safety among 2SLGBTQ+, women, BIPOC, or trauma-affected members of the avalanche and mountain guiding profession. It draws on in-depth interviews conducted in Canada, USA, Switzerland and France within a profession that is overwhelmingly White, cis-gender, hetero, and male. Psychological safety was defined by research participants as feeling safe enough to show one’s full self and to speak up when feeling unsafe due to either mountain or human-caused hazards based on the principles of trust, interconnection, and reciprocity within a group. Factors that supported psychological safety were (in order of strength in the data) vulnerability, teamwork, physical safety, and gender diversity. Psychological risk in relation to human-caused hazards was found to significantly affect physical safety from mountain hazards for both members of the profession and their clients. Human-caused hazards in the profession include exclusion, harassment, and discrimination based on identity factors, such as stigma related to trauma or mental health challenges, racism, ableism, sexism, misogyny. The study revealed that while significant work remains to be done to ensure psychological safety within the avalanche and guiding profession, there is also much to celebrate. Human-caused hazards are entirely dependent on human choice, and are determined by human agency. Members of non-dominant groups demonstrated their abilities to carve out psychologically safe-enough spaces to be able to thrive with authenticity within a competitive, hierarchical and hyper-masculine professional culture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".