Cyberbullying among Emerging Adults: Exploring Prevalence, Impact, and Coping Methods
Bibliographic record
Abstract
Cyberbullying has been a concern among adolescents, parents and educators for years for its seemingly boundless reach and potential harm it can cause. Aggressors are often masked with anonymity and targeted individuals may feel powerless over what others do online. While such issues and concerns are prevalent among adolescent ages, college students are not invulnerable from parallel experiences. In the US, Macdonald and Roberts-Pittman (2010) found less than ten percent of college students were cyberbullies but over one-fifth reported as cybervictims. This study explored prevalence of cyber victimization and perpetration as well as evidence of damaging effects and impact. Among the college student sample of 1,921, participants were 18-25 years (mean age 20.1), just over half Caucasian (55.5%) and female (67.9%). Results indicated victimization most often occurred through phones (19.9%) and social networks (20.4%). For perpetration, prevalence was low across all platforms, however phone use was the preferred means of attacking others (6.5%). Low self-esteem was a significant predictor for victimization and perpetration. For males only, social capital was a significant predictor of victimization. Future directions and recommendations for follow-up studies are discussed as well as the importance of this study in relation to college student activities and behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".