Social Framework Testimony and Race Salience: Examining Bias Correction in the Current Context
Bibliographic record
Abstract
This juror-simulation study tested whether expert testimony about police relations with Black/Indigenous persons would mitigate potential verdict discrepancies by making race a salient issue, and whether perceived police legitimacy would predict perceptions of race salience and/or effectiveness of the salience manipulation. Jury-eligible community members (N = 392) read a trial transcript in which the defendant claims self-defense for the killing of a police officer. We manipulated defendant race (Black/Indigenous/White) and the presence of expert testimony in which a sociologist described the experience of racialized persons with police. Participants provided verdicts, rated perceptions that racial issues featured prominently in the trial (i.e., perceived race salience), and completed a police legitimacy measure. Results revealed non-significant effects of defendant race and expert testimony on verdicts. Those higher in perceptions of police legitimacy had a greater likelihood of voting guilty and less favourable attitudes toward the expert, with the opposite pattern for those higher in perceived race salience.
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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 | 0.012 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".