Adversity in University: Cyberbullying and Its Impacts on Students, Faculty and Administrators
Bibliographic record
Abstract
This paper offers a qualitative thematic analysis of the impacts of cyberbullying on post-secondary students, faculty, and administrators from four participating Canadian universities. These findings were drawn from data obtained from online surveys of students and faculty, student focus groups, and semi-structured interviews with faculty members and university administrators. The key themes discussed include: negative affect, impacts on mental and physical health, perceptions of self, impacts regarding one's personal and professional lives, concern for one's safety, and the impact of authorities' (non) response. Students reported primarily being cyberbullied by other students, while faculty were cyberbullied by both students and colleagues. Although students and faculty represent different age levels and statuses at the university, both groups reported similar impacts and similar frustrations at finding solutions, especially when their situations were reported to authorities. It is important that universities pay greater attention to developing effective research-based cyberbullying policies and to work towards fostering a more respectful online campus culture.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".