EXAMINING THE THEORY OF AVERSIVE RACISM: DOES DEFENDANT IMMIGRANT STATUS AND ETHNICITY, AND JUROR GENDER CONTRIBUTE TO JUROR BIAS?
Bibliographic record
Abstract
Implicit bias by jurors towards immigrants in the United States legal system has become a main focus within law and psychology literature. Aversive racism theory suggests that people may hold egalitarian values, however, they may unconsciously hold negative attitudes about out-groups and express them very indirectly and subtly. The purpose of this study was to examine prejudicial attitudes towards immigrants by European American mock jurors and examine if the theory of aversive racism could best explain such prejudice. In a mock juror study, 283 European American participants were randomly assigned to one of four conditions in a 2 (immigration status: legal or illegal) X 2 (Country of origin: Canada or Mexico) between-groups design. The measured variable of juror gender was also examined (gender: male or female) to complete eight conditions in a 2X2X2 between-groups design. Participants acted as mock jurors and read a case trial transcript, provided a verdict, recommended a sentence, answered various questions regarding culpability, rated the defendant on a number of trait measures, answered questions pertaining to the specific details of the crime and defendant, and provided personal demographic information. Based on prior research, it was hypothesized that European American male jurors would find undocumented immigrant defendants from Mexico guilty significantly more often, recommend lengthier sentences, more culpable, and rate them more negatively on trait measures compared with all other conditions. Jurors demonstrated bias based on the interactive effects of the independent measures. Limitations and future directions are discussed.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".