The Changing World of Bullying: Student Strategies for Cyberbullying Intervention
Bibliographic record
Abstract
A gap in research on cyberbullying intervention strategies exists. The purpose of this study was to identify effective coping strategies for cyberbullying by interviewing cyberbullying survivors. The study used grounded theory qualitative methodology to allow data to fully emerge from participants’ perspectives. When analyzing the data, the researchers found that youth engaged in three types of coping: online coping, offline coping, and intrinsic coping. Online coping involved online interventions to stop active cyberbullying and prevent future cyberbullying; for example, youth limited who had access to their online accounts or blocked and reported cyberbullies. Offline coping included strategies that participants engaged in offline to minimize, tolerate, or cope with the effects of cyberbullying, such as talking about their experiences or reframing the way that they think about things. Finally, intrinsic coping described survivors’ personality traits or ways of being that aided them in developing such resilient coping strategies; for instance, possessing self-awareness and self-love contributed to survival. Accordingly, the findings contribute to the literature on effective coping strategies by confirming previously identified strategies, like online coping, and highlighting new ones, like intrinsic coping. The findings also help inform future counseling practices within schools.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".