Who Would You Tell? A Canadian Youth’s Perspective of Reporting Incidents of Cyberbullying
Bibliographic record
Abstract
Background: Inappropriate communication technology, electronic bullying, Internet harassment, cyber aggression. These are all terms that describe the pervasive and difficult issue known as cyberbullying (Tokunaga, 2010). Cyberbullying has been linked to psychosocial issues, substance use, behavioural and school problems (Berne et al., 2013). Purpose: Using previous data from two exploratory studies of cyberbullying among secondary students in Saskatchewan, we aimed to better understand student reactions to cyberbullying and the reasons for those reactive decisions. Methods: Quantitative data was collected from 396 students in rural and urban areas of Saskatchewan. Also, qualitative follow-up data was collected by employing two semi-structured focus groups with students to provide a better understanding of the quantitative results. Results: Both quantitative and qualitative data suggested that students are apprehensive about reporting to parents when they face instances of cyberbullying. They are also unlikely to seek help from a teacher. Fortunately, the majority of students noted that they would be likely to tell a friend or other family member if it were serious. Conclusion: Cyberbullying continues to be an important issue of study among Canadian students. Further research is needed to understand the approaches and reasons that students take when they face instances of 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.037 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".