Adolescents’ Evaluation of Cyberbullying Events
Bibliographic record
Abstract
Educators and other professionals working with adolescents have grown increasingly concerned about how technology affects social relationships given the amount of time that is spent engaging in online activities. Cyberbullying has sparked the interest of many researchers due to the tragic events reported in the media, relating to the online victimization that adolescents have experienced. Cyberbullying has been defined as any intentional and aggressive message, repeated over time against someone who is not able to defend him or herself using electronic communication devices (Menesini & Nocentini, 2009; Smith et al. 2008). This definition is similar to the classical definition of bullying, which includes three elements: intention to harm, repetition over time, and power imbalance (Olweus, 2001). Similar to the research on the disastrous effects of direct and indirect aggression in children and adolescents (Juvonen, Nishina & Graham, 2001), cyberbullying has also demonstrated negative effects on victims. The sequelae of cyberbullying include: distress, negative emotions, and frustration (McQuade, Colt, & Meyer, 2009). Although there is an increased interest in cyberbullying, most of the current research is focused on the frequency of cyberbullying (Hinduja & Patchin, 2008; Kowalski & Limber, 2007; Li, 2008; Raskauskas & Stoltz, 2007) and on co-occurrence of bullying in school settings and online (Li, 2006; Ybarra & Mitchell, 2004; Ybarra, Mitchel, Finkelhor, & Wolak, 2007). Regarding the moral evaluation of cyberbullying, it has been found that cyberbullies display low levels of moral values and emotions (Perren & Gutzwiller-Helfenfinger, 2012). Investigating moral aspects of cyberbullying is important so that we can understand why adolescents engage in these kinds of actions. By knowing how adolescents judge events online, educators can design more effective interventions aimed at preventing cyberbullying. The purpose of the current study was to examine children’s moral evaluations of cyberbullying. Additionally, this study was interested in understanding which characteristics of cyberbullying adolescents consider crucial to be classified as a bullying event. Our hypotheses were as follows: (1) adolescents would evaluate situations that surround lying and intention to harm as more negative, and (2) girls will evaluate cyberbullying as more negative than boys.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".