Cyber-Bystander Behavior Among Canadian and Iranian Youth: The Influence of Bystander Type and Relationship to the Perpetrator on Moral Responsibility
Bibliographic record
Abstract
The current study examines how social determinants influence the way youth from Canadian and Iranian contexts evaluate and morally disengage as bystanders of cyberbullying. While Iranian culture differs from other individualistic and collectivist cultures, Iranian youth have become just as technologically acculturated as their global peers. Despite this, less is understood about how Iranian youth respond to cyberbullying in comparison to youth from individualistic societies. Participants from Canada (N = 60) and Iran (N = 59) who were between the ages of 8-to-15 years old (N = 119, M = 11.33 years, SD = 1.63 years) read 6 cyberbullying scenarios that varied according to Bystander Relationship to Perpetrator (Acquaintance or Friend) and Bystander Response (Assists Cyberbully, Does Nothing, Defends Victim). After reading each scenario, participants were asked to evaluate the bystander's behavior. They were also asked how they would feel if they were the bystander. Similar to past research, these responses were coded on a continuous scale ranging from morally disengaged to morally responsible. Overall, Canadians were more critical of passive bystander behaviors and more supportive toward defending behaviors compared to Iranians. Iranians were more supportive of the behaviors of bystanders who were friends of perpetrators than Canadians were, and Iranians were more critical toward acquaintances of perpetrators. Significant interactions were also found between participants' country of origin, the bystander's relationship with the perpetrator and the bystander's behavior. Taken together, these findings highlight the importance of differentiating between negative judgments and moral attributions of bystander responses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".