Hazing in Canadian university athletics: The influence of gender and sport type
Bibliographic record
Abstract
Hazing has been found to be a common experience among college/university and high school varsity athletes (Allan & Madden, 2008; Hoover, 1999; 2000) and has been linked to negative physical and psychological outcomes. (Brakenridge, 1997; Finkel, 2002; Nuwer, 2000). Despite the potential seriousness of hazing activities, little is known about the factors that influence who experiences these activities and no examination of hazing prevalence has been undertaken in Canada. The purpose of the present study was to assess the prevalence of hazing in a sample of Canadian university athletes and examine the influence of gender and sport type (collision or non-collision) on the experience of hazing as a victim. Participants included 338 athletes from 27 sports teams at seven Canadian universities. Participants completed questionnaires assessing their hazing experiences. The results indicated that over the course of their careers, more than 92% of participants had experienced at least one hazing activity as a rookie, with nearly 72% involved in alcohol-related hazing activities and 47% participating in unacceptable hazing activities. The impacts of gender and sport type were examined using 2 (gender) X 2 (degree of contact) ANOVAs. Surprisingly, significant differences between men and women were not found. Conversely, a strong effect for sport type was identified: collision sport athletes were the most likely to have experienced hazing. These findings extended across various levels of hazing severity suggesting that the phenomenon of hazing is more closely tied to sport culture than gender. Implications of the findings for sport psychology practitioners are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".