Pre-Service Teachers’ Perceptions about Identifying, Managing and Preventing Cyberbullying
Bibliographic record
Abstract
Cyberbullying uses technology to deliberately and repeatedly humiliate, harass, or threaten someone with the intention to cause reputational damage, harm, or intimidation. It is a widespread issue that impacts teaching and learning in schools, as well as in the larger community. Cyberbullying has garnered much attention in schools, social media, and also from researchers. Within teacher education programs, how are we preparing pre-service teachers to have the knowledge and skills to identify, manage, and prevent cyberbullying. This paper explores pre-service teachers’ beliefs and perceptions of cyberbullying, drawing on data from online discussions, Archived online discussions were analyzed, using a constant comparison method. Pre-service teachers’ perceptions and concerns about identifying, managing and preventing cyberbullying are discussed. The paper concludes with three implications for teacher education.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".