Ending the cycle: Scholars' perspectives on hazing prevention
Bibliographic record
Abstract
The purpose of this study is to synthesize the knowledge of hazing experts to explore hazing prevention techniques. Preventing hazing in sport has been studied, analyzed, and explored through various research designs centered on strategies of athlete education, cultural change, and replacement activities. However, literature has lacked a study that compares and integrates these methods within the practice of prevention. This study begins to conceptually fill that void. To critically examine, compare, and integrate hazing prevention methods, published hazing scholars were surveyed using the Delphi technique. All participants had published at least one peer-reviewed publication on hazing written in English. Using the Delphi technique, participants were surveyed three times, with each iteration being developed from the results of the previous survey. The first survey had fifteen participants, the second had fourteen, and the third had eleven. The survey responses were analyzed using thematic coding. Participating scholars provided detailed descriptions of best practices with prevention strategies centered on athlete education, cultural change, and replacement activities. Importantly, participating scholars identified that all hazing prevention methods should be implemented as much as possible as they are often connected, interrelated, and have the potential to be most effective when utilized together.
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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 | 0.075 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.029 | 0.055 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.011 | 0.015 |
| 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".