Exploring the use of systematic and heuristic processing in the courtroom: the effect of evidence modality on jurors’ decision-making processes
Bibliographic record
Abstract
The use of technology in the courtroom is increasingly commonplace. While some research has explored how technology may influence jurors throughout the trial itself, there has been little focus on how it might influence jurors during the deliberation period, or whether it affects their verdicts. The current study assessed whether the form of evidence available during the decision-making period, along with the mock juror’s level of motivation for the task, affects how trial information is processed and how verdict decisions are made. Mock-jurors (N = 243), half of whom were explicitly informed of the task’s importance, watched a video of a murder trial. During the decision-making phase, some jurors were then given the opportunity to review the trial video, transcript, or both before rendering a final verdict. While there were no differences in verdicts as a function of review condition, the amount of content mock-jurors reviewed differed by review condition.
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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 | 0.014 | 0.145 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".