What makes hazing acceptable? Examining the predictors of student and student-athletes' ratings of hazing acceptability
Bibliographic record
Abstract
Athletic hazing is a problem across North American high school and university campuses (Allan & Madden, 2008; Hamilton, Scott, O'Sullivan, & LaChapelle, 2013). Although hazing is often defined as "any activity expected of someone joining a group that humiliates, degrades, abuses, or endangers, regardless of the person's willingness to participate" (Hoover & Pollard, 1999), there is still some ambiguity regarding what specific actions and behaviours are encompassed within this definition. What some might consider an acceptable initiation activity, others might consider to be hazing. As such, the purpose of this study was to assess which factors (gender, sport-level, past participation, moral disengagement, attitudes about initiation and hazing, etc.) affect and initiation activity's level of acceptability. Participants included 386 students and student-athletes from various Canadian universities. The results indicated that past participation in an activity (as a perpetrator or victim) was the biggest predictor of ratings of hazing acceptability. Other significant predictors included gender, moral disengagement, attitudes, and preference for consistency. The broader theoretical and practical implications of these findings will be discussed in detail.Acknowledgments: University Research Endowment Fund
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".