I’ll be there for you? The bystander intervention model 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 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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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