Modeling the bullying prevention program design recommendations of students from grades five to eight: a discrete choice conjoint experiment
Bibliographic record
Abstract
We used a discrete choice conjoint experiment to model the bullying prevention recommendations of 845 students from grades 5 to 8 (aged 9-14). Students made choices between experimentally varied combinations of 14 four-level prevention program attributes. Latent class analysis yielded three segments. The high impact segment (27.1%) recommended uniforms, mandatory recess activities, four playground supervisors, surveillance cameras, and 4-day suspensions when students bully. The moderate impact segment (49.5%) recommended discretionary uniforms and recess activities, four playground supervisors, and 3-day suspensions. Involvement as a bully or bully-victim was associated with membership in a low impact segment (23.4%) that rejected uniforms and surveillance cameras. They recommended fewer anti-bullying activities, discretionary recess activities, fewer playground supervisors, and the 2-day suspensions. Simulations predicted most students would recommend a program maximizing student involvement combining prevention with moderate consequences. The simulated introduction of mandatory uniforms, surveillance cameras, and long suspensions reduced overall support for a comprehensive program, particularly among students involved as bullies or bully-victims.
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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".