Bibliographic record
Abstract
Please note: This article contains some text used by the author in other publications. This study examines preservice teachers’ perceptions about cyberbullying. Specifically, the following questions guide the research: (i) To what extent are preservice teachers concerned about cyberbullying? (ii) How confident are preservice teachers in managing cyberbullying problems? (iii) To what extent do preservice teachers feel prepared to deal with cyberbullying? (iv) To what extent do preservice teachers think that school commitment is important? Survey data were collected from 154 preservice teachers enrolled in a two-year post-degree program in a Canadian university. The results show that although a majority of the preservice teachers understand the significant effects of cyberbullying on children and are concerned about cyberbullying, most of them do not think it is a problem in our schools. In addition, a vast majority of our preservice teacher have little confidence in handling cyberbullying, even though the level of concern is high. Résumé : La présente étude examine la perception des futurs enseignants à l’égard de la cyberintimidation. Plus précisément, les questions suivantes ont orienté la recherche : (i) Dans quelle mesure les futurs enseignants sont-ils préoccupés par la cyberintimidation ? (ii) À quel point les futurs enseignants sont-ils confiants dans leur capacité de gérer des problèmes de cyberintimidation ? (iii) Dans quelle mesure les futurs enseignants se sentent-ils prêts à faire face à la cyberintimidation ? (iv) Dans quelle mesure les futurs enseignants pensent-ils que l’engagement de l’école est important ? Les données de l’enquête ont été recueillies auprès de 154 enseignants non encore à l’emploi inscrits dans un programme de deux ans aux cycles supérieurs dans une université canadienne. Les résultats montrent que bien que la majorité des futurs enseignants comprennent les effets significatifs de la cyberintimidation sur les enfants et soient préoccupés par ce phénomène, la plupart d’entre eux ne pensent pas que la cyberintimidation constitue un problème dans nos écoles. En outre, une grande majorité de nos futurs enseignants s’avèrent peu confiants dans leur capacité de gérer la cyberintimidation, même si leur niveau de préoccupation est élevé.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".