Context Masculinities: Media Consumption, Physical Education, and Youth Identities
Bibliographic record
Abstract
This article reports findings from a qualitative case study undertaken at a Vancouver high school designed to examine the role of media and physical education (PE) in shaping how young males ( n = 36) understand and practice gender. The authors were specifically concerned with developing a nuanced understanding of how interpretations of masculinity relate to performances of masculinity and how interpretations and performances vary depending on social context and according to a youth’s social positioning. Findings revealed how participants criticized media portrayals of “hegemonic masculinities” (i.e., muscular, aggressive, and hyper-heterosexual masculinities) while simultaneously celebrating these same gender identities in PE. The authors used these findings as a foundation for arguing that (a) youth are flexible in their interpretations/performances of masculinity according to context; (b) the participants, in critiquing portrayals of hypermasculinity in media and supporting less radical versions of hegemonic masculinity in PE, were creating personal narratives around masculinity as a potential strategy for coping with feelings of disembodiment and disembeddedness; and (c) students’ critiques of gender portrayals, although offering symbolic challenges to the contemporary gender order, are nonetheless limited by structures that normalize hegemonic masculinities.
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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 | 0.003 | 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 | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".