An exploration of Canadian elementary and secondary teachers’ experiences, perceptions and responses to identity-based bullying
Bibliographic record
Abstract
Identity-based bullying (IBB) is behavior rooted in discrimination, where someone is targeted due to an actual or perceived social identity. Certain identities increase the likelihood of being bullied. Bullying based on discrimination can have detrimental impacts. Teachers can reduce bullying and contribute to a positive school climate, yet there is limited research on teacher experiences with IBB. Utilizing a diverse sample of 1005 Canadian elementary and secondary school teachers, this study explored how frequently teachers witness various forms of IBB; how often students report IBB to them; and teachers’ perceptions and intentions to intervene in response to ethnicity-, academic performance-, weight-, sexual orientation-, race,- and religious-based bullying, through the use of descriptive analyses and two-way ANOVAs. Most teachers have witnessed IBB, and many have received student reports of IBB. Ethnicity, religious, and race-based scenarios garnered greater likelihood of intervention, were perceived as more serious and important to respond to, compared to weight-, sexual orientation-, or academic performance-based bullying. Additionally, responding to religious-based bullying was perceived as more important compared to sexual orientation-based bullying. Analyses for teacher responses found that elementary teachers were more likely to encourage bullies and victims to work together in response to IBB as compared to secondary teachers. Implications for prevention and intervention 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".