Attribution of human characteristics and bullying involvement in childhood: Distinguishing between targets
Bibliographic record
Abstract
This investigation researched the association between the attribution of human characteristics and bullying involvement in children by distinguishing between targets. Study 1 focused on the attribution of human characteristics by bullies, victims, bully/victims, and non-involved children toward friends and non-friends. The data from 405 children (M = 10.7 years old) showed that they attributed fewer prosocial and more antisocial human characteristics to non-friends than to friends. Moreover, boy victims attributed fewer prosocial human characteristics to non-friends than boy bullies and non-involved boys did. In addition, victims attributed more antisocial human characteristics to non-friends than non-involved children did. Study 2 addressed bullies', victims', bully/victims', and non-involved children's attribution of human characteristics to each other. The data of 264 children (M = 10.0 years old) showed that bullies, victims, and bully/victims attributed fewer prosocial and more antisocial human characteristics to each other than to non-involved children. Non-involved children attributed fewer prosocial human characteristics to bully/victims than to bullies and victims, and more antisocial human characteristics to bully/victims than to victims. In addition, girls attributed more prosocial and fewer antisocial human characteristics to girls than to boys, whereas boys did not distinguish between girls and boys. Based on these findings, suggestions for future research are provided and implications for bullying prevention and intervention are discussed. Aggr. Behav. 42:394-403, 2016. © 2015 Wiley Periodicals, Inc.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".