Bullying: Are researchers and children/youth talking about the same thing?
Bibliographic record
Abstract
Given the rapid increase in studies of bullying and peer harassment among youth, it becomes important to understand just what is being researched. This study explored whether the themes that emerged from children's definitions of bullying were consistent with theoretical and methodological operationalizations within the research literature, and whether the provision of a definition when administering bullying experience items would lead to different prevalence rates in reported victimization and bullying. Students aged 8—18 ( N = 1767) were randomly assigned to one of two conditions. In the first condition, students were provided with a standard bullying definition; in the second condition, students provided their own definition of bullying. Results indicated that students' definitions of bullying rarely included the three prominent definitional criteria typically endorsed by researchers: intentionality (1.7%), repetition (6%), and power imbalance (26%), although almost all students (92%) did emphasize negative behaviors in their definition. Younger children made more mention of physical aggression, general harassing behaviors, and verbal aggression in their definitions, whereas the theme of relational aggression was most prominent in the middle years and reported more by girls than boys. Finally, students who were given a definition of bullying reported being victimized less than students not provided with a definition. As well, boys who were given a definition of bullying tended to report higher levels of bullying than those not given a definition (marginal effect).
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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 | 0.038 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".