Culture in the Courtroom: Ethnocentrism and Juror Decision-Making
Bibliographic record
Abstract
The purpose of this study was to investigate whether a culturally-based argument in a non-insane automatism defense would be detrimental or beneficial to the defendant. We also examined how juror ethnocentrism might affect perceptions of such a defense. Participants read a fictional filicide homicide case in which the defendant claimed to have blacked out during the crime; we manipulated whether culture was used as an explanation for what precipitated the defendant's blackout. We conducted path analyses to assess the role of ethnocentrism in predicting lower defendant credibility, and harsher verdict decisions. Results revealed an interaction between ethnocentrism and defense type, such that ethnocentrism related to lower perceived defendant credibility in the cultural condition, but not in the standard automatism condition. This study marks a starting point for empirically investigating the role of culture in the courtroom, which may aid scholars in discussing the merits of a standalone cultural defense.
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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 | 0.003 | 0.019 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".