Bibliographic record
Abstract
Cyberbullying is a problem that has emerged as a byproduct of modern day technologies.This novel form of peer aggression occurs when one or more individuals use a technological medium for the purposes of threatening or harming others (Belsey, 2004).Given that cyberbullying is a relatively new problem in Canada, research remains in its preliminary stages.Previous studies conducted in large urban centers in Alberta and Quebec have suggested that cyberbullying frequently occurs among middle years students (Beran & Li, 2005;Li, 2006Li, , 2007;;Shariff, 2008).However, the characteristics of cyberbullying among rural students and students from other Canadian provinces are yet to be determined.For these reasons, the purpose of this study was to explore cyberbullying amongst students from rural and urban schools in Saskatchewan.More specifically, this study investigated the following questions:1. To what extent did youth experience cyberbullying? 2. What were the characteristics of cyberbullying? 3. How did students respond to cyberbullying? 4. To what extent did parents and teachers become involved with cyberbullying incidents?Furthermore, how did students think these adults should have responded?To answer these questions, 396 students from a large public school division in central Saskatchewan completed an anonymous paper pencil questionnaire.Among the grades 7 to 9 students sampled, 34.6% admitted they cyber-bullied others and 49.5% said they were victims of cyberbullying.Further, the majority (69.4%) of the students reported that they knew someone who had been cyber-bullied.No significant differences were found between urban and rural students' experiences with cyberbullying.However, significant gender differences were found iii as well as significant correlations between cyberbullying involvement and student grade level, frequency of computer use, school size, and school type.Unfortunately, the majority of cyber-bully victims and bystanders chose not to report the incident to adults.They reported a variety of negative outcomes, especially anger and sadness.Students offered many suggestions for the prevention and intervention of cyberbullying.In particular, students thought teachers should educate their class about cyberbullying and parents should talk to their children about the issue.thesis research.Heartfelt thanks go to the various principals and teachers who opened their classroom doors to me.Without their cooperation this study would not have been possible.Nearly 400 students provided their input on cyberbullying.I appreciate their generosity in the sharing of their time and expert knowledge. My sincerest gratitude goes to my thesis supervisor, Dr. Laurie Hellsten
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".