Cyberbullying in Schools: Nature and Extent of Canadian Adolescents' Experience
Bibliographic record
Abstract
This study is an exploration of the cyberbullying issue. The primary focus is on the examination of the nature and extent of adolescents’ cyberbullying experiences. Particularly, the following research questions guide this exploration: 1) To what extent do adolescents experience cyberbullying? 2) What are the characteristics of cyberbullying? 3) What are the types of tools used for cyberbullying? A survey of 177 grade seven students (80 males and 97 females) was analyzed to answer the research questions. The results show that almost 54% of the students were bully victims and over a quarter of them had been cyber-bullied. More than half of the students knew someone being cyber-bullied. Over 40% cyberbully victims had no ideas who cyber-bullied them. Further, there was a close tie among bullies, cyberbullies, and cyberbully victims. Introduction School violence is a serious social problem both in Europe (Clarke & Kiselica, 1997; Hoover & Juul, 1993) and North America (Hoover & Olsen, 2001; Charach, Pepler, & Aiegler, 1995). This problem is particularly persistent and acute during junior high/middle school period (National-Center-for-Educational-Statistics, 1995). Possible reasons explaining this high frequency of school violence observed include the drastic biological and social changes experienced by adolescents. “[A]dolescence is a period of abrupt biological and social change. Specifically, the rapid body changes associated with the onset of adolescence and changes from primary to secondary school initiate dramatic changes in youngster’s peer group composition and
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".