“Making Kind Cool”: Parents' Suggestions for Preventing Cyber Bullying and Fostering Cyber Kindness
Bibliographic record
Abstract
Cyber bullying among youth is rapidly becoming a global phenomenon, as educators, parents and policymakers grapple with trying to curtail this negative and sometimes devastating behavior. Since most cyber bullying emanates from the home computer, parents can play an important role in preventing cyber bullying and in fostering a kinder online world, or what might be termed “cyber kindness.” In this study, we examine parents' knowledge of social networking technology, their level of concern with cyber bullying, their experiences with cyber bullying, and their ideas for preventing cyber bullying and promoting cyber kindness. Three hundred and fifteen parents from three schools in British Columbia, Canada completed a questionnaire, primarily involving open-ended, written responses. We found that parents are not very familiar with the newer forms of online social networking, such as Facebook, blogs, and chat rooms. Further, they are not overly concerned about the problem of cyber bullying, nor are they aware of the extent of cyber bullying among their children. Although a minority of parents looked to stricter controls over technology and more stringent punishment as the solution, most parents thought a more effective way, in the long-term, was for adults in the home and school to model the right behavior, provide opportunities to dialogue with youth, and develop school curricula on this theme. The results demonstrate the need for collaboration among students, parents, and educators.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".