Juror motivations: applying procedural justice theory to juror decision making
Bibliographic record
Abstract
Jurors sometimes consider inadmissible evidence in their verdicts, despite judicial instructions to disregard that evidence. Procedural justice research suggests this is because jurors are motivated to prioritise just outcomes over due process; thus, jurors’ non-compliance towards judicial instructions to disregard inadmissible evidence may be the product of a discrepancy between legal and lay peoples’ understanding of the juror’s role. In this mixed-method design, we examined 294 university students’ a priori perceptions about the role and responsibility of jurors, and empirically tested how randomly assigning participants to the role of a juror (versus a judge’s associate/assistant, who helps the judge to ensure a trial is conducted according to proper procedure) influenced their verdict decisions, prioritisation of outcome versus procedural considerations, motivation to protect the community, and perceived obligation to ensure correct procedures. Overall, the results demonstrated ambivalence in lay people’s perceptions of the juror role, with many participants perceiving jurors to be responsible for protecting society; however, we did not find support for our predictions that participants assigned the role of a juror (versus judge’s associate) would more strongly prioritise outcomes over procedures. Methodological issues, recommendations for future research, and implications are also 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 | 0.059 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".