Cyberbullying among University Students: Gendered Experiences, Impacts, and Perspectives
Bibliographic record
Abstract
Cyberbullying is an emerging issue in the context of higher education as information and communication technologies (ICT) increasingly become part of daily life in university. This paper presents findings from 1925 student surveys from four Canadian universities. The overall findings are broken down to determine gender similarities and differences that exist between male and female respondents’ backgrounds, ICT usage, experiences with cyberbullying, opinions about the issue, and solutions to the problem. We also examine the continuities between these findings and those of earlier studies on cyberbullying among younger students. Our findings also suggest that gender differences, which do emerge, provide some support for each of the three theoretical frameworks considered for understanding this issue, that is, relational aggression, cognitive-affective deficits, and power and control. However, none of these three models offers a full explanation on its own. The study thus provides information about cyberbullying behaviour at the university level, which has the potential to inform the development of more appropriate policies and intervention programs/solutions to address the gendered nature of this behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".