Faculty Members' Perceived Experiences and Impact of Cyberbullying from Students at a Canadian University: A Mixed Methods Study
Bibliographic record
Abstract
This mixed methods study was conducted at a Canadian University in 2012, using an online survey and individual interviews to explore faculty members’ perceived experiences of having aggressive, intimidating, defaming, or threatening message(s) sent to them or about them by students via electronic media. Limited empirical research on this issue within the context of higher education led the researcher to draw from literature on workplace bullying, academic bullying, and K-12 sector cyberbullying, of which theoretical frameworks have included student development, power, aggression, and group theories. This study explored cyberbullying through the theoretical lenses of power, disinhibition, and victimization. Consistent with previous bullying and cyberbullying research, this study found that faculty members who had encountered at least one significant cyberbullying incident (it had a negative effect on them) experienced detrimental physical, emotional, relational, and professional effects. Demographic data such as age, rank, and gender are discussed, in addition to the duration of effects, support measures sought, and support measures recommended by cyberbullied faculty members. Study findings not only serve to inform the workplace and cyberbullying literature of this phenomenon, but provide a foundation for the development of institutional policy and education programs in the prevention and management of cyberbullying.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".