To defend or not to defend a cyber victim: The role of individual and situational characteristics in motivating active cyber defending among university students
Bibliographic record
Abstract
This research investigated the role of individual and situational characteristics involved in motivating active cyber-defending online.Undergraduate students (N = 278, Mage = 19.10)were given questionnaires examining online bystander behaviour, moral disengagement, empathy, self-efficacy, social power, their relationship to the victim, the psychosocial cost of defending, and demographic questions.Three hypotheses were investigated.First, individuals low on moral disengagement, high on empathy, defender self-efficacy, social power, know the victim, and do not expect there to be a high psychosocial cost for defending will help the victim.Second, it was hypothesized that people who are low on moral disengagement, high on empathy, low on defender self-efficacy, social power, do not know the victim, and expect a high psychosocial cost will be less likely to defend, acting as passive cyber bystanders.Third, reinforcing the cyber-bully online was expected to be associated with high moral disengagement, low empathy, defender self-efficacy, high social power, not knowing the victim, and not expecting a high psychosocial cost to defend.Active cyber defending was related to high defender self-efficacy, whereas low defender self-efficacy, popularity, and high moral disengagement predicted remaining passive online.Conversely, reinforcing the cyber-bully online was predicted by high moral disengagement and low empathy.These results have implications for furthering our understanding of cyber-defending online and can inform the development of intervention and prevention programs aimed at promoting active defending online.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".