Cyberbullying in Western Australia and New Zealand high schools
Bibliographic record
Abstract
The internet provides some of the most effective means of communication. But alongside the positive aspects of the internet, cyberbullying is certainly one of the most negative aspects, especially with regard to school students. Cyberbullying occurs when the internet is used to bully another person. Victims of cyberbullying may be able to obtain legal sanctions, however, this usually occurs after the harm is done. In Australia, some states, including South Australia, have recognised the need for preventive strategies by requiring schools to have an anti-bullying plan in place, as stated in their 'Children and Young People (Safety) Act 2017' (SA). Other jurisdictions, like Ontario, Canada have implemented similar preventative strategies in legislation, such as the 'Education Act 1990'. This article explores cyberbullying in high schools, as research has shown that cyberbullying is most prevalent among school-aged children, mainly those between the ages of 13–15 years and decreases as they age.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".