Every Face Has a Name: Individuation Training Reduces Implicit Racial Bias
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it