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Record W3017422183 · doi:10.1080/14729679.2020.1755706

Hegemonic masculinity in outdoor education

2020· article· en· W3017422183 on OpenAlexaff
Jay Kennedy, Constance Russell

Bibliographic record

VenueJournal of Adventure Education & Outdoor Learning · 2020
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsLakehead University
Fundersnot available
KeywordsHegemonic masculinityMasculinityHegemonyScholarshipHeterosexismSociologyGender studiesField (mathematics)Curriculum studiesPedagogyPolitical scienceHomosexualityCurriculumPoliticsLaw

Abstract

fetched live from OpenAlex

Increasing attention is being paid to gender in outdoor education, with scholars and practitioners sharing experiences of sexism and heterosexism and explicitly calling for an examination of hegemonic masculinity in the field. The purpose of this paper is to respond to that call by: summarizing scholarship on hegemonic and alternative masculinities; reviewing research on masculinities in education that is particularly relevant to outdoor education; pointing to existing work in outdoor education that lays a foundation for examining masculinity, including promising recent research; and offering suggestions for disrupting hegemonic masculinity and creating conditions for more diverse gender performances. Since there has been so little research on masculinities in outdoor education thus far, there remain significant unanswered questions. It is past time for focused examination of hegemonic masculinity in the field as one strategy for addressing gender inequity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.024
Scholarly communication0.0060.004
Open science0.0000.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.340
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations40
Published2020
Admission routes1
Has abstractyes

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