Cyberbullying of professors: what measures are in place in universities and what solutions are proposed by victims?
Bibliographic record
Abstract
Cyberbullying in the workplace is a growing phenomenon and universities are no exceptions. As teachers and researchers, university professors interact online with a diversity of people, placing them in a vulnerable position towards cyberbullying. Despite this situation, measures in universities are not well known and studies on the subject are scarce. The present article tackles this issue in presenting the results of a mixed-method research that aimed to analyze (1) measures in place associated with cyberbullying in universities and (2) solutions proposed by professors. To collect quantitative and qualitative data, a questionnaire (n = 202) was sent online and interviews (n = 9) were conducted with professors from two universities in Quebec, Canada. Besides confirming that measures in place associated with cyberbullying are largely unknown by professors, the results of the research show that they are often insufficient to manage the complexity and the diversity of professors’ cyberbullying incidents. To address this complexity, answers given by professors on possible solutions to prevent cyberbullying, manage incident and support victims were inductively analyzed. Solutions emanating from this analysis are presented such as empowerment, policy implementation and, clarity and independence of the reporting process. Implications of these solutions for future research and for universities are also discussed.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".