Cyberbullying and Social Media
Bibliographic record
Abstract
Abstract Recent years have seen an increase in research devoted not only to cyberbullying broadly speaking but to cyberbullying and social media specifically. With the majority of 13–17-year-olds using social media apps such as YouTube or TikTok on a regular basis, there are concomitant increases in involvement in cyberbullying as victim and/or perpetrator, both within the United States and around the world. Outcomes of cyberbullying for victims and perpetrators include heightened levels of depression, anxiety, and suicidal ideation, often accompanied by lower levels of self-esteem. Much of the cyberbullying that occurs among adolescents on social media platforms involves current or former classmates, placing schools in the difficult position of having to decide whether to intervene or not. Considering these issues, in this chapter, we examine the current state of the research focusing on cyberbullying involvement and social media, including policy and legal issues, uncover limitations of the existing research, and identify ways forward for future researchers and practitioners.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".