The Influence of Gender and Age in Mock Juror Decision-Making
Bibliographic record
Abstract
This study examines the influence of demographic variables on mock juror decision-making in a case of psychopathy. The gender of a fictitious criminal defendant who was labeled a psychopath was manipulated in order to examine the potential prejudicial impact on mock juror’s decision-making. Additionally, juror demographics (gender, age, and education level) were used to identify the source of bias. Participants read a fictitious manslaughter scenario followed by a psychologist’s expert testimony. Participants were asked to make a decision regarding the guilt of the defendant, and if applicable, specify the sentence the guilty defendant should receive. Findings from previous research looking at gender bias were not replicated, however, results showed a significant interaction between juror age and verdict/sentencing type. The youngest age group recommended a guilty verdict and a sentence of probation more often than the older age groups. In contrast, the older age groups were more likely than the youngest age group to give a verdict of not guilty, and sentence of incarceration (when a guilty verdict was given). These results raise concerns regarding views of the justice system when a designation of psychopathy is involved, as well as differences in cognitive processing that occur across ages.
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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 | 0.005 | 0.046 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".