How Jurors Weigh Dispositional Risk Factors in Assessing False Confession Claims
Bibliographic record
Abstract
This research project investigates how Canadian potential jurors evaluate the believability of a suspect’s claim that their confession was false. Specifically, the study aims to identify which dispositional risk factors for false confessions most strongly influence juror judgments. False confessions continue to be a significant concern within the criminal justice system, often leading to wrongful convictions despite contradictory evidence, such as DNA mismatches. Understanding how laypeople (including potential jurors) process confession evidence in light of dispositional risk factors is critical to enhancing the reliability of verdicts and reducing the risk of wrongful convictions. This research will be conducted using a policy-capturing design, which allows for the systematic manipulation of multiple factors across a series of hypothetical case scenarios. In each scenario, participants will be asked to rate how believable they find the suspect’s claim that their confession was false, on a scale from 1 (completely unbelievable) to 10 (completely believable). This study focuses on dispositional risk factors, examining how five characteristics commonly associated with increased false confession risk (compliant personality, youth, trauma history, low intellectual ability, and mental illness history; Kassin et al., 2025) impact juror perceptions. Each factor will be presented in a "conducive" (i.e., risk-enhancing) or "non-conducive" (i.e., low-risk or absent) version, and scenarios (as well as the factors within them) will be randomized to control for any potential order effects. The primary goals of this research is to: - Quantify the relative weight jurors assign to each factor when evaluating disputed confession claims; - Explore individual differences in cue use, including how many and which factors participants rely on in their decision-making; - Determine whether certain cues dominate judgments, potentially overshadowing others; and - Assess how consistently participants apply reasoning across scenarios, using within-subject modelling. Expected outcomes include identifying a subset of dispositional risk factors (e.g., youth, mental illness) that are particularly influential in participants' judgments, and in turn, revealing variability in juror decision-making or reasoning strategies, and uncovering gaps in lay understanding of dispositional risk factors for false confessions. These findings can inform jury instructions, admissibility decisions regarding expert testimony, legal policy, and educational efforts aimed at reducing wrongful convictions stemming from false confessions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 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; both teacher heads agree on what is shown here.
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".