Challenging Jurors' Racism
Bibliographic record
Abstract
Despite overwhelming documentation of disproportionate arrest, prosecution, conviction, and incarceration of Black Americans and the many psychological tools available to assess racism and implicit bias, antiracist jury selection remains an understudied area of research. An evidence-based jury selection process is an urgent need, particularly due to the historical and ongoing racial bias within this process. This article attempts to assist judges and attorneys in their efforts to select an impartial jury by equipping them with a better understanding of different forms of racism (e.g., overt, covert, symbolic, aversive) as well as providing an introduction to insightful psychometric tools that can be used to prioritize the selection of antiracist jurors and identify those who hold implicit and explicit biases or others who are likely to be impartial in their assessment of the case. Attorneys have tended to focus on a person’s race or self-reported attitudes to identify racism in potential jurors, but these approaches do not effectively identify aversive racists, who need to be asked direct questions about their behaviors rather than their attitudes. Likewise, efforts to identify racial justice allies amongst potential jurors should be employed to identify people who are able to approach their jury duty in a courageous anti-racist manner. Some tools discussed include the Color-Blind Racial Attitudes Scale (CoBRAS), Symbolic Racism Scale 2000 (SR2K), Modern Racism Scale (MRS), Multigroup Ethnic Identity Measure (MEIM), Black-White Implicit Association Test (BW-IAT), and the Interpersonal Racial Allyship Scale (IRAS). We also propose a novel questionnaire created by the authors that is specifically designed to differentiate racial justice allies from aversive racists. We hope this article will help to inform the development of a more robust system to select antiracist jurors, and ultimately help to create a more just American legal system.
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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 | 0.088 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".