Cyberbullying of professors: what measures are in place in universities and what solutions are proposed by victims?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it