Hegemonic masculinity and rape culture: negotiating manhood at a Canadian university
Bibliographic record
Abstract
Using in-depth qualitative interviews with 12 male-identified individuals between the ages of 19 and 25 recruited from the Memorial University community, this project examines how men negotiate the complexities of hegemonic masculinity in a society in which that hegemonic masculinity is constructed as violent. This research is prompted by the current crisis in North America of campus rape culture and grounded in the need to identify solutions to the rate of sexual violence occurring in university communities. To examine masculinities in this context, I explore this key question: How do male-identified individuals understand and negotiate the intricacies of masculinity and societal expectations of what it means to “be a man”? to explore this, I turn to the following sub-questions: a) to what extent do participants identify with hegemonic notions of masculinity? b) what developmental experiences have most influenced their self-identification (either positively or negatively) with traditional masculine norms? c) how is rape culture understood, perceived and perpetuated (or challenged) by male-identified university students? Drawing on the theoretical work of gender researchers such as Raewyn Connell and Michael Kimmel, this thesis will include insights into the ways in which masculinities are being reproduced, challenged and resisted.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.046 | 0.023 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".