Not sticks and stones but tweets and texts: findings from a national cyberbullying project
Bibliographic record
Abstract
This paper presents key findings from a project commissioned by a group of young people to explore issues related to cyberbullying with 12–18-year olds. In particular, the paper focuses on those findings related to impact and support needs. The project collected data through a web-based Survey Monkey questionnaire and focus groups. A total of 473 young people aged 11–19 years in England responded to the questionnaire and 17 young people aged 10–18 took part in the focus groups: 19.7% (n = 87) admitted that they had been cyberbullied, just under half the young people in this study knew someone who had been cyberbullied and a similar proportion of girls and boys admitted having cyberbullied others. Most of the young people thought cyberbullying was as harmful as traditional face to face bullying. But while a few thought it could be more serious, others thought it less serious or even non-existent. Over a quarter of those who had been cyberbullied stayed away from school and over a third stopped socialising outside school. However, just over half the total sample said they did not worry about cyberbullying. The finding that 78% of those that sought support did so by talking to their parents contrasts starkly with previous research. Further research is needed to explore what makes some young people more resilient than others to cyberbullying; the role of the school and parents in dealing with cyberbullying.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".