Online Moral Disengagement, Cyberbullying, and Cyber-Aggression
Bibliographic record
Abstract
The study of moral disengagement has greatly informed research on aggression and bullying. There has been some debate on whether cyberbullies and other cyber-aggressors show more or less of a tendency for moral disengagement than traditional aggressors and bullies. However, according to the triadic model of reciprocal determinism, an individual's behavior influences and is influenced by both personal factors and his/her social environment. This article reviews the literature to propose a new conceptual framework addressing how features of the online context may enable specific mechanisms that facilitate moral disengagement. Specific affordances for moral disengagement proposed here include the paucity of social-emotional cues, the ease of disseminating communication via social networks, and the media attention on cyberbullying, which may elicit moral justification, euphemistic labeling, palliative comparison, diffusion and displacement of responsibility, minimizing and disregarding the consequences for others, dehumanization, and attribution of blame. These ideas suggest that by providing affordances for these mechanisms of moral disengagement, online settings may facilitate cyber-aggression and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".