Moving from Cyber-Bullying to Cyber-Kindness
Bibliographic record
Abstract
The purpose of this chapter is to explore cyber-bullying from three different, but interrelated, perspectives: students, educators and parents. The authors also explore the opposite spectrum of online behaviour - that of “cyber-kindness” - and whether positive, supportive or caring online exchanges are occurring among youth, and how educators, parents and policy-makers can work collaboratively to foster a kinder online world rather than simply acting to curtail cyber-bullying. These proactive efforts tackle the deeper causes of why cyber-bullying occurs, provide students with tools for positive communication, open the door for discussion about longer term solutions, and get at the heart of the larger purposes of education – to foster a respectful and responsible citizenry and to further a more caring and compassionate society. In the course of this discussion, they highlight the findings from two studies they conducted in British Columbia, Canada, one on cyber-bullying and a later study, which addressed both cyber-bullying and cyber-kindness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".