Aboriginal Youth Experiences with Cyberbullying
Bibliographic record
Abstract
Technology has transformed interactions among adolescents from face-to-face to instantaneous virtual communication. Yet the use of digital media among adolescents can be potentially harmful with the risk of cyberbullying. While cyberbullying is a growing concern, few researchers have explored cyberbullying experiences among Aboriginal adolescents. The present study addresses this gap by examining qualitative data regarding cyberbullying experiences provided by Aboriginal youth participants between ages 11 and 17 in Aboriginal e-mentoring BC, which was an internet-based mentoring program in the province of British Columbia, Canada. The analysis of the data highlighted 4 themes: (1) perceptions and use of technology, (2) awareness of online safety and netiquette, (3) cyberbullying prevalence, and (4) prevention and coping skills. Transcending these themes was the importance of Aboriginal perspective and knowledge in mentoring and anti-cyberbullying initiatives. The results of the work presented in this study highlight the potential benefit of incorporating online safety and technology use in interventions to promote wellbeing among Aboriginal youth. The study findings on Aboriginal adolescents’ online experiences and perceptions of online safety can assist researchers and Indigenous health providers to better understand the cyberbullying phenomenon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".