Do You Stand By or Stand Up? Bystander Characteristics in Social Bullying and Cyberbullying
Bibliographic record
Abstract
The aim of the current study was to investigate how moral disengagement and defender self-efficacy were related to bystander behaviour in social and cyberbullying. Four hundred and ninety-five emerging adults completed an online survey consisting of two measures of moral disengagement, a measure of defender self-efficacy and an adapted version of the Student Bystander Behaviour Scale. Regression analyses revealed that moral disengagement for the whole sample was positively associated with pro-bully behaviour and that defender self-efficacy was positively related to defender behaviour in both the social and cyberbullying contexts. The findings revealed that in order to better explain bystander behaviours, researchers should consider multiple cognitive mechanisms involved in bullying across various contexts. This study demonstrated the necessity of investigating social bullying and cyber bullying across various developmental periods and in turn may inform intervention efforts on how to encourage individuals to defend others when confronted with various forms of bullying
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".