Online Moral Disengagement, Cyberbullying, and Cyber-Aggression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it