Cyberbullying Perpetration: Children and Youth at Risk of Victimization during Covid-19 Lockdown
Bibliographic record
Abstract
The Covid-19 is believed to have emerged in Wuhan, China, and has affected many countries across the globe. In response to this pandemic, governments in different countries have implemented social distancing measures to stop the spread of the virus. The closure of schools and switch to remote learning of universities to protect youth and children from exposure to the virus might also open opportunities for certain crimes such as cyberbullying. The study aimed at exploring the risks of victimization of children and youth through cyberbullying during the lockdown. A qualitative approach, non-participant observation was utilised. Data was collected from three social media platforms which include Facebook, Twitter, and Instagram from posts since the beginning of lockdown. Keywords such as “ama2000s”, “2000s” and “90s vs 2000s” were used to search for content. Facebook groups for “2000s” where most young people engage were also used. The study found that with the increase of the use of social media among children and youth during the lockdown, most have been victims of cyberbullying. In these platforms where young people engage, most posts and comments carried content which includes sexting, sexual comments on young girls’ pictures, trending of videos of school children fighting, and insulting each other. A significant finding was the use of fake accounts to perpetrate cyberbullying. The study recommends that addressing cyberbullying through educating children and youth about acceptable online behaviour, signs of cyberbullying, responses to it, and cybersecurity should be prioritised.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".