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Record W4387578236 · doi:10.1080/13573322.2023.2264909

Understanding gender disproportion and influences on subject choice in Physical Health Education: a British Columbia high school case study

2023· article· en· W4387578236 on OpenAlexaffabout
April St. Louis, Manu Sharma

Bibliographic record

VenueSport Education and Society · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsSubject (documents)Dominance (genetics)PsychologyPhysical educationGender studiesSocial psychologyDevelopmental psychologySociologyPedagogy

Abstract

fetched live from OpenAlex

The aim of this qualitative case study is to examine the phenomenon of female students’ disproportionate representation in senior level Physical Health Education (PHE) classes. The subject for this teacher’s Masters level project arose from the first author’s personal observations while teaching across several schools in British Columbia, Canada. Through semi-structured interviews with self-identified female participants, we explore female subject choice and if, along with possible reasons why, females may avoid participation in senior level (grade 11-12) mixed gender PHE activity courses. This discussion asserts that females mostly make conscious choices to avoid mixed gender courses at the senior level for several key reasons; females feel silenced by their male peers, PHE is not an inclusive environment, and females perceive their male counterparts assert dominance over them in PHE class. This paper continues with a discussion regarding reinforced gendered norms and their effect on perceived barriers to female participation in schools, and concludes with suggestions for how schools and educators might support female students’ participation.

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)0.001
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: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.158
GPT teacher head0.488
Teacher spread0.330 · 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
Published2023
Admission routes2
Has abstractyes

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