The Impact of Minority-Race Status on the Cross-Race Effect: A Critical Review
Bibliographic record
Abstract
Meta-analyses have consistently demonstrated the robustness of the cross-race effect (CRE; i.e., better recognition of same-race faces compared with different-race faces). These analyses have unveiled variations in the dependent variables associated with the CRE across combinations of participant and target races. However, the underlying factors driving these variations remain poorly understood. We posit that although the CRE is robust, its generalizability may be contingent on the specific racial groups compared, particularly when contrasting majority and minority racial groups. In this article, we delve into the dynamics of the CRE across distinct racial groups and explore how minority-race status may influence research outcomes. We considered the articles included in the latest meta-analyses of the CRE with a spotlight on minority-race status. We suggest that minority-race status may explain why many studies considering non-White participants do not show a CRE. The CRE might not be as robust as it appears to be because much of the research on the effect has focused on majority-race participants and minority-race faces. Going forward, researchers should consider incorporating measures relevant to the minority effect, fully crossing participant and target races and studying a greater variety of races.
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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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".