Juror decision-making concerning defendants with mental health conditions – a systematic review of experimental studies
Bibliographic record
Abstract
This systematic review explores the methodological characteristics, features and findings of empirical research adopting an experimental mock juror design to investigate legal decision-making regarding defendants with mental health conditions. A systematic search was conducted using MEDLINE, CINAHL, PsycINFO, PsyArticles and Web of Science, with thirty-two eligible studies included within the final review. Study quality was assessed using the Appraisal Tool for Cross-Sectional Studies (AXIS). All studies were conducted across the United States and Canada, with the exception of one conducted in the United Kingdom. Studies varied significantly in their aims, sampling, variables manipulated and other methodological characteristics. Many effects were reported as non-significant, although a range of significant aggravating and mitigating effects were found in relation to the effect of different diagnostic terms, types of evidence presented and other defendant or participant characteristics on mock jurors’ verdict and sentencing decisions. Inconsistencies in direction of effect were found even amongst the higher quality studies. Strengths, limitations, and recommendations for future research 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 | 0.032 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".