Attributions in the courtroom: the influence of race, incentive, and witness type on jurors’ perceptions of secondary confessions
Bibliographic record
Abstract
Research has shown that jurors are heavily influenced by secondary confessions, and that they may attribute the informant’s motives to good character rather than to an incentive. This study investigated the role of race in this context by manipulating both defendant and informant race (Black/White), informant type (jailhouse/civic duty), and whether the informant received an incentive to testify. Participants read a trial transcript and provided a verdict, then answered questions about the informant’s reason for testifying (i.e. attributions). We observed that in the absence of informant testimony, participants convicted the White defendant more often. We also discovered an effect of incentive on verdicts when the defendant was White, such that participants voted guilty less often when the informant received an incentive; there was no effect of incentive on verdict when the defendant was Black. Informant race, defendant race, and incentive showed a combined effect on verdict, such that participants were particularly suspicious (i.e. less likely to vote guilty) when a Black informant received an incentive for testifying against a Black defendant. There were no effects of race on attributions. This research sheds light on extralegal factors that can prevent jurors from considering the role of incentives in secondary confessions.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".