An International Investigation of Variability in Teacher Perceptions of Bias-Based Bullying and Their Likelihood of Intervening
Bibliographic record
Abstract
Abstract Bias-based bullying (i.e., bullying due to marginalized identities) is a significant and ongoing challenge within contemporary educational settings. Teachers are crucial in mitigating such harmful behaviors and cultivating positive peer relationships. The present study explores teachers’ perceptions of and intervention intentions in bias-based bullying situations across diverse cultural and educational settings. Using a between-subjects experimental design, primary and secondary teachers from 13 international sites located in Africa, Asia, Australia, Europe, and North America ( n = 4990) were randomly assigned to read a hypothetical vignette depicting student victimization based on their ethnicity, learning difficulties, physical appearance, gender expression, or sexual orientation. Multilevel linear models revealed that teachers’ perceptions varied depending on the type of bias-based bullying, such that when teachers were presented with a situation of bullying based on gender expression or sexual orientation, they reported lower levels of perceived responsibility, self-efficacy, and importance of responding when compared to other types of bullying. At the same time, teachers were less likely to blame the victim of bullying and expressed greater empathy towards involved students when being presented with a situation of weight-based bullying. However, there were no significant differences in rated intervention intentions across conditions. Results have important implications for teacher education and development, as well as for existing anti-bullying intervention programs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".