The Dark Side Of The Ivory Tower: Cyberbullying Of University Faculty And Teaching Personnel
Bibliographic record
Abstract
This paper discusses findings from an exploratory study on the nature, extent, and impact of cyberbullying experienced by 121 faculty members at one Canadian university. We situate cyberbullying in university on a continuum between cyberbullying in K-12 education and cyberbullying in the workplace and also take into account the power dynamics that characterize the post-secondary context. Quantitative and qualitative analyses of online survey data revealed that 17% of respondents had experienced cyberbullying either by students (12%) or by colleagues (9%) in the last 12 months. Gender differences were apparent plus racial minority status also appeared to render faculty members more vulnerable to cyberbullying. These findings suggest a rights-based lens could be used to analyze and respond to the vulnerabilities of women and other marginalized faculty in cyberbullying situations. This study contributes to the dearth of research on cyberbullying at the post-secondary level and raises the need to consider factors of difference, such as gender and race, in policy development and practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".