Learning by Example: Does Positive Nonverbal Behavior Reduce Children's Racial Bias?
Bibliographic record
Abstract
Nonverbal behavior is a ubiquitous, everyday cue that is often used as a basis for social evaluation. Numerous studies indicate that children are sensitive to these signals and form evaluative judgments after viewing positive or negative nonverbal cues directed toward a target. Furthermore, they generalize these judgments to other members of a targets' social group, indicating that nonverbal behavior displays can influence intergroup bias. However, no studies thus far have directly examined whether exposure to positive nonverbal behavior cues can reduce children's implicit and explicit racial bias. In the current study, we exposed White and Asian children ages 9-11 to positive nonverbal behavior displayed by a White expresser toward a Black target, drawn from children's television shows. Children demonstrated a pro-White/anti-Black bias implicitly, but explicitly preferred Black over White characters. Additionally, children judged Black characters from the clips and novel Black characters positively. We found that there was no difference in implicit or explicit racial bias between children who viewed positive nonverbal behavior demonstrated by a White expresser to a Black target as compared to children who were only exposed to a Black target (and no nonverbal cues) or unrelated video clips. Future research examining the influence of positive nonverbal behavior on children's racial bias should consider using more overt or prolonged demonstrations of positive nonverbal behavior or increasing children's familiarity with the characters presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".