Bibliographic record
Abstract
Identity-based bullying encompasses physical and verbal aggression rooted in unique traits such as race and gender, particularly within school contexts. Extensive literature underscores its detrimental impact on the well-being of victimized youth. Teachers' pivotal role in intervening is acknowledged, though a gap exists between incident rates and interventions, partly attributed to educators' misperception of bullying occurrences. Through the lens of social identity theory, which examines how individuals identify themselves, this study investigated whether teachers' past experiences in bullying-related roles shape their perceptions and interventions, influenced by contextually salient social identities triggered by situational cues. Additionally, the study explored how empathy and victim blaming may enhance or weaken this relationship, respectively. Data were collected through a self-administered online survey, where teachers provided demographic information and were randomly assigned one of five identity-based peer exclusion scenarios. After a manipulation check, 941 participants ranked and answered questions assessing perceptions and responses to the scenario, including variables like seriousness, responsibility, empathy, victim blaming, and intervention likelihood. Participants' bullying-related roles were self-reported based on a section where they described their role as a witness, defender, bully, or victim during their school years. Contrary to our hypothesis, there are unexpected findings on teachers' response with prior experience as victims. Furthermore, bullying-related roles were not moderated by empathy and victim-blaming, with one exception. Overall, the findings from this study allows us to comprehensively understand these dynamics, which in turn inform strategies that optimize teacher interventions and foster a supportive environment for students' growth and well-being.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".