I’ll be there for you? The bystander intervention model and cyber aggression
Bibliographic record
Abstract
The Bystander Intervention Model (BIM) has been validated for face-to-face emergencies and dictates that observers’ decision to intervene hinges on five sequential steps, while barriers block progress between steps. The current study is the first, to our knowledge, to apply the BIM in its entirety to cyber aggression and explore the ways that individual factors such as experiences with depression, social anxiety, and cyber aggression either as the target or the aggressor influence bystanders. In our pre-registered study, emerging adults (N = 1,093) viewed pilot-tested cyber aggressive content and reported how they would engage with each of the steps and barriers of the BIM, if they were observing this content as a bystander in real life. Regarding the actions they would take, most participants chose non-intervention (36.3%) or private direct intervention (39.4%). Path analysis suggested that overall, the BIM can explain bystanders’ responses to cyber aggression. Nonetheless, there were some discrepancies with prior work on face-to-face emergencies, specifically that cyber bystander intervention does not appear to be as linear. As well, in contrast to the face-to-face applications of the BIM that prescribes barriers to affect only a single specific step, here we found some barriers were negatively linked to multiple steps. These findings elucidate ways in which cyber aggression in the online context may be similar to, as well as different from, aggression that occurs face-to-face. Implications of these findings for interventions are discussed.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".