Understanding gender disproportion and influences on subject choice in Physical Health Education: a British Columbia high school case study
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".