Cyberbullying among Saudi’s Higher-Education Students: Implications for Educators and Policymakers
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
The aim of the current study was to investigate cyberbullying among Saudi’s higher-education students. It also aimedto identify possible factors that may impact cyberbullying. A quantitative approach was implemented using an onlinesurvey questionnaire distributed to 287 students. The descriptive results indicated that students mainly avoidcyberbullying. However, about 27% of the students reported that they have committed cyberbullying at least once ortwice. Furthermore, 57% of the students observed at least one student being cyberbullied. Students encountercyberbullying usually by people whom they do not know and who contacted them over the Internet. In addition,students perceive cyberbullying as a serious issue. Thus, students seem to prefer asking cyberbullies to stop, butavoiding fighting back. Gender was found to impact on how often did students commit cyberbullying. Male studentswere involved in cyberbullying more than female students. In addition, single students more than married studentsencounter cyberbullying by people they know. Finally, students who access the Internet via personal devices observecyberbullying more than those using shared devices. Based on this, implications were analyzed and suggested wereproposed in relation to policy and practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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