Online Moral Disengagement: An Examination of the Relationships Between Electronic Communication, Cognitive Empathy, and Antisocial Behavior on the Internet
Bibliographic record
Abstract
A consequence of the proliferation of online communication is the concerning presence of antisocial behavior observed in virtual spaces. Research suggests the cognitive component of empathy is hindered by features of electronic communication which facilitates antisocial behaviors online. Investigations into how features of online communication inhibit cognitive empathy are lacking, and findings on moral disengagement and antisocial behavior have yet to be integrated with studies on cognitive empathy and electronic communication. The current study explores these relationships. One hundred and three undergraduate students completed several measures including the Online Moral Disengagement Scale, Questionnaire of Cognitive and Affective Empathy, and Online Prosocial and Antisocial Behavior Scale. Results showed a positive correlation between compulsive internet use and online moral disengagement, as well as a negative correlation between cognitive empathy and moral disengagement online. It was hypothesized that online moral disengagement would mediate the relation between cognitive empathy and antisocial behavior online but this mediation was not supported. However, a moderated relationship was revealed between cognitive empathy and moral justification, by liberalism. This moderation can be explored further and built upon by future research to deepen our understanding of how political ideology relates to virtual behavior. Furthermore, the findings concerning components of empathy and moral disengagement, and their role within the perpetration of antisocial conduct online, can inform future research as well as interventions focused on fostering prosocial behavior online and curbing cyberaggression.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".