Every Face Has a Name: Individuation Training Reduces Implicit Racial Bias
Bibliographic record
Abstract
ABSTRACT Addressing racial bias in early childhood is crucial for fostering inclusivity and reducing social inequalities. This study examined the effectiveness of individuation training in reducing racial bias among Canadian preschool‐aged children and explored how interracial contact might influence changes in children's implicit anti‐Black bias. A total of 113 preschool‐age children (60 females, M age = 5.31 years) were trained to individuate Black or White faces. Results showed a significant reduction in implicit anti‐Black bias following Black individuation training, whereas no significant change was observed in the White individuation training group. Additionally, factors such as interracial friendships were found to influence the reduction of bias. These findings contribute to the understanding of developmental interventions for diverse cultural contexts, with implications for early childhood education and efforts to promote social inclusivity. A video abstract of this article can be viewed at https://www.powtoon.com/c/enBEKBMdMXR/1/m
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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 | 0.000 |
| 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".