What Makes Race Salient? Juror Decision-Making in Same-Race Versus Cross-Race Identification Scenarios and the Influence of Expert Testimony
Bibliographic record
Abstract
Research demonstrates that juror race may interact with defendant race to influence decision-making, but little work has investigated interactions with eyewitness race. This study tested whether Black/White jurors would produce different perceptions/decisions when faced with a Black/White defendant identified by a Black/White eyewitness. We also examined the influence of expert testimony regarding the cross-race effect in two floating cells. Mock jurors read a trial transcript, provided a verdict and trial party ratings, and indicated perceived race salience. Black jurors were more likely to convict a White defendant identified by a Black eyewitness than a Black defendant identified by a White eyewitness. Expert testimony was valued more highly when the defendant was Black, but had no direct influence on verdict; however, it raised race salience perceptions (as did presence of Black trial parties). Perceived race salience was associated with lower rates of conviction, suggesting that race and expert testimony have potential courtroom implications.
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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 | 0.005 | 0.035 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".