Men's Perspectives on Gender Relations in the Outdoor Education Field: Furthering the Case for a Hybrid Masculinity
Bibliographic record
Abstract
Background: Scholarship has demonstrated the influence of hegemonic masculine norms on values and practices in outdoor adventure education. However, recent publications indicate that men outdoor leaders may be increasingly aware of gender biases and consequently changing their practice. To date, few publications have considered men outdoor leaders’ understanding of masculinity in the field and how it affects their practice and professional interactions. Purpose: This study critically examined multiple aspects of gender relations from men outdoor leaders’ perspectives to determine if observed changes in some men's practice signal changes toward a more equitable understanding of masculinity or merely a pivot to maintain the status quo. Methodology/Approach: A single-embedded case study methodology was employed. Interview, observation, and artifact data were collected from 18 men outdoor adventure education leaders across the United States and Canada. Findings/Conclusions: Participants noted awareness of gender inequity and articulated some strategies employed to combat sexism, particularly those associated with gendered student expectations. However, multiple participants also demonstrated gendered blind spots and supported gender hierarchy. Implications: Participants’ demonstrated awareness of gender issues, but the considerable blind spots identified in the data indicate a troubling lack of critical self-reflection and provide support for dominant hybrid masculinity.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".