The Interactive Effects of Race and Expert Testimony on Jurors’ Perceptions of Recanted Confessions
Bibliographic record
Abstract
We examined the effect of defendant race and expert testimony on jurors' perceptions of recanted confessions. Participants (591 jury-eligible community members) read a first-degree murder trial transcript in which defendant race (Black/White) and expert testimony (present/absent) were manipulated. They provided verdicts and answered questions regarding the confession and expert testimony. When examining the full sample, we observed no significant main effects or interactions of defendant race or expert testimony. When exclusively examining White participants, we observed a significant interaction between expert testimony and defendant race on verdicts. When the defendant was White, there was no significant effect of expert testimony, but when the defendant was Black, jurors were significantly more likely to acquit when given expert testimony. These findings support the watchdog hypothesis, such that White jurors are more receptive to legally relevant evidence when the defendant is Black.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".