A Comparative Analysis of Cyberbullying Perceptions of Preservice Educators: Canada and Turkey.
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
Canadian preservice teachers (year one N= 180 & year two N= 241) in this survey study were compared to surveyed preservice educators in Turkey (N=163). Using a similar survey tool both Turkish and Canadian respondents agreed that cyberbullying is a problem in schools that affects students and teachers. Both nations agreed that children are affected by cyberbullying however a lack of confidence was found in the Canadian sample yet Turkish educators believed they could both identify and manage cyberbullying. Cyberbulling in comparison to other topics covered in the current teacher preparation program, was believed to be equally important. Preservice teachers in both countries believed they should use an anti-cyberbully infused curriculum which had activities and current resources. A school-wide approach, in combination with professional development coupled with counselling from community supports was perceived to be essential to deal with cyberbullying in each country. Parents and community members were believed to be essential as was the idea that various media sources should be used to reach the larger community. As a result of their university training both Turkish and Canadian respondents felt unprepared to deal with cyberbullying.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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