International Perceptions of Cyberbullying Within Higher Education
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
In this study, we investigated perceptions of cyberbullying within higher education among 1,587 professionals from Australia, Canada, the United Kingdom, and the United States. Regardless of country or professional role, participants presented essentially the same bleak picture. Almost half of all participants observed cyberbullying between students within the last year, about one in every five intervened in an incident, and only 10% felt completely prepared to do so. Likewise, 85% of participants perceived their institution to be less than completely prepared to handle cyberbullying, with fewer than 50% even aware whether their school had a cyberbullying policy and fewer than 25% having a policy that specifically addresses cyberbullying. The majority of participants perceived cyberbullying as negative; however, approximately 10% dissented from this view. Finally, a group-serving bias was replicated; cyberbullying was perceived as more problematic at other institutions than their own. This research calls for evidence-based, systematic policy development and implementation, including how to train those who see cyberbullying as a positive phenomenon.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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