#RookieParty: The Intersections of Social Media, Hazing, and Canadian Varsity Sport
Bibliographic record
Abstract
Prior to the start of the pandemic in 2020, almost two-thirds of all varsity athletes across gender and sport in Canada had engaged in a sport hazing ritual (Johnson et al., 2018). While hazing in sport has been researched from a variety of perspectives, few studies have examined hazing in the context of varsity athletes' social media use. This is a potential space where Johnson (2017) suggested that technological tools such as cellphones, that provide instant access to social media, have enabled users to more easily capture and post online, potentially exacerbating the trauma inflicted on rookies during team hazing events. This qualitative study engaged semi-structured interviews to examine the intersections of hazing and social media Canadian varsity athletes. The results describe the use of social media by athletes within the context of their sport and their particular team and its relationship to their continued team hazing practices. It also offers a gender-based comparison of the ways in which athletes interpreted both formal and informal messaging they received from their athletic departments and teams about hazing and/or social media. The key determinants for the athletes' use of social media in sport were using their social media platform(s) as a team-focused promotion tool, a communication tool, and a motivator for rivalry. Differences emerged as men athletes used social media for recruitment, mentorship and as a sport promotional tool. With respect to using social media for hazing communications, the women athletes were primarily concerned with protecting their teammates’ well-being and ensuring the goal of hazing on their team was centered on creating stronger bonds, while men engaged in clandestine and self-protecting social media messaging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".