Perceptions of Masculinity among Male Varsity Rugby Players at Queen’s
Bibliographic record
Abstract
Existing literature on the topic of sport and masculinity has suggested that male varsity athletes model more hegemonic masculine norms (Messner, 2002). In fact, athletic participation has been found to be a predictor of misogynistic and homophobic attitudes (Steinfeldt et al., 2011). It has been argued that these attitudes are further enforced by the fact that the social power possessed by male athletes receives institutional support, which can in turn influence the social and sexual cultures on university campuses (Sanday, 2007). Contact team sports have a reputation for reinforcing hegemonic masculinity more than other sports do (Messner, 2002). Rugby is a particularly aggressive and male-dominated sport (Maxwell & Visek, 2009), however the majority of studies on varsity athletics and masculinity use data from American colleges and focus on contact sports that are historically more prominent in North America such as football and hockey (Steinfeldt et al., 2011; Messner, 2002; Boeringer, 1996). I hope to add to the existing body of research by focusing exclusively on rugby at a Canadian University. To do so, I will conduct interviews with 5 men who are current players on the Queen’s varsity rugby team. I will perform a content analysis on the transcripts of the interviews to assess how male varsity rugby players at Queen’s University understand and express masculinity. I intend to distribute my findings to Queen’s athletic administrators and rugby coaching staff. The findings may contribute to leadership training that addresses gender issues in athletics at Queen’s.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".