What Antibullying Program Designs Motivate Student Intervention in Grades 5 to 8?
Bibliographic record
Abstract
Educators detect and intervene in a small proportion of bullying incidents. Although students are present when many bullying episodes occur, they are often reluctant to intervene. This study explored attributes of antibullying (AB) programs influencing the decision to intervene. Grade 5, 6, 7, and 8 students (N = 2,033) completed a discrete choice experiment examining the influence of 11 AB program attributes on the decision to intervene. Multilevel analysis revealed 6 latent classes. The Intensive Programming class (28.7%) thought students would intervene in schools with daily AB activities, 8 playground supervisors, mandatory reporting, and suspensions for perpetrators. A Minimal Programming class (10.3%), in contrast, thought monthly AB activities, 4 playground supervisors, discretionary reporting, and consequences limited to talking with teachers would motivate intervention. Membership in this class was linked to Grade 8, higher dispositional reactance, more reactance behavior, and more involvement as perpetrators. The remaining 4 classes were influenced by different combinations of these attributes. Students were more likely to intervene when isolated peers were included, other students intervened, and teachers responded quickly. Latent class analysis points to trade-offs in program design. Intensive programs that encourage intervention by students with little involvement as perpetrators may discourage intervention by those with greater involvement as perpetrators, high dispositional reactance, or more reactant behavior.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".