A Comparative Analysis of Cyberbullying Perceptions of Preservice Educators: Canada and Turkey.
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".