Dehumanization in children: The link with moral disengagement in bullying and victimization
Bibliographic record
Abstract
The current study explored subtle dehumanization-the denial of full humanness-in children, using distinctions of forms (i.e., animalistic vs. mechanistic) and social targets (i.e., friends vs. non-friends). In addition, the link between dehumanization and moral disengagement in bullying and victimization was investigated. Participants were 800 children (7-12 years old) from third to fifth grade classrooms. Subtle animalistic and mechanistic dehumanization toward friends and non-friends were measured with the new Juvenile Dehumanization Measure. Results showed that animalistic dehumanization was more common than mechanistic dehumanization and that non-friends were dehumanized more than friends. The highest levels of dehumanization were found in animalistic form toward non-friends and the lowest levels in mechanistic form toward friends. Both moral disengagement and animalistic dehumanization toward friends were positively associated with bullying. However, moral disengagement was negatively associated with victimization, whereas both animalistic and mechanistic dehumanization toward non-friends were positively associated with victimization. The current findings indicate that children are able to distinguish different forms and targets of dehumanization and that dehumanization plays a distinct role from moral disengagement in bullying and victimization.
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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 | 0.000 | 0.001 |
| 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".