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Record W7057787511

Leadership in Mountain and Wildland Professions in Canada: Examining the Impacts of Gender, Safety, and Climate Change

2022· book-chapter· en· W7057787511 on OpenAlexaboutno aff

Bibliographic record

VenueBern Open Repository and Information System (University of Bern) · 2022
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsHarassmentClimate changeNarrativeMasculinityCompetence (human resources)OffensiveWork (physics)Action (physics)
DOInot available

Abstract

fetched live from OpenAlex

This chapter explores mountain and wildland environments as socially constructed masculine spaces where competence as a professional is linked to performing certain types of masculinity. The findings shared are from two studies conducted using a feminist appreciative approach to Action Research methods: A 2016 study amongst wildland firefighters in British Columbia, and a 2019 study amongst avalanche and guiding professionals in Western Canada. These professionals work in environments that are becoming increasingly risky due to climate change. The conflation of competence with masculinity is revealed to have negative impacts on wellbeing for all members of mountain and wildland professions, including harassment and discrimination, and increased suicide rates for cis-males. Notably, this includes negative impacts to team decision making, risk management, safety, and inclusion. Ultimately, the authors expose “masculinity as competence” to be a socio-cultural myth. While this myth is dominating the cultural discourse among professionals working in mountain and wildland environments at present, there is an emergent space for new culturally inclusive narratives in these environments.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.738
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.066
GPT teacher head0.222
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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