Optimizing Population Screening of Bullying in School-Aged Children
Bibliographic record
Abstract
A two-part screening procedure was used to assess school-age children's experience with bullying. In the first part 16,799 students (8,195 girls, 8,604 boys) in grades 4 to 12 were provided with a definition of bullying and then asked about their experiences using two general questions from the CitationOlweus Bully/Victim Questionnaire (1996). In the second part, students were asked about their experiences with specific types of bullying: physical, verbal, social, and cyber. For each form of bullying, students were provided with several examples of what constituted such behavior. Results indicated that the general screener has good specificity but poor sensitivity, suggesting that the general screening questions were good at classifying noninvolved students but performed less well when identifying true cases of bullying. Accordingly, reports from the World Health Organization, UNICEF, and the United Nations may underestimate the prevalence of bullying among school-aged children world-wide.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.001 | 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".