Rating the Honesty of White and Black Children <i>via</i> Implicit and Explicit Measures: Implications for Child Victims in the Criminal Justice System
Bibliographic record
Abstract
The present study explored implicit and explicit honesty perceptions of White and Black children and whether these perceptions predicted legal decisions in a child abuse case. Participants consisted of 186 younger and 189 older adults from the online Prolific participant pool. Implicit racial bias was measured via a modified Implicit Association Test and explicit perceptions through self-reports. Participants read a simulated legal case where either a Black or White child alleged physical abuse against their sports coach, and they rated the honesty of the child's testimony and rendered a verdict. Participants were implicitly biased to associate honesty with White children over Black children, and this bias was stronger among older adults. In the legal vignette, for participants who read about a Black child victim, greater implicit racial bias predicted less trust in the child's testimony and a lower likelihood of convicting the coach of abusing the child. In contrast to their implicit bias, participants self-reported Black children as being more honest than White children, suggesting a divergence in racial attitudes across implicit and explicit measures. Implications for child abuse victims are discussed.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".