International Perceptions of Cyberbullying Within Higher Education
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".