Comparing jury focus and comprehension of expert evidence between adversarial and court-appointed models in Canadian criminal court context
Bibliographic record
Abstract
The present adversarial system is often criticised for not working as well as it should in the area of expert scientific testimony. Yet scientific opinion evidence is an important aspect of present criminal trials. In addition to issues in the provision of expert evidence, triers of fact are challenged to understand complex scientific evidence. Several dynamics are at play that may impact on their ability to focus on and comprehend the science, and alternative models have been suggested to address these issues, including the use of court-appointed experts. This study examines juror focus on the science versus the persona/demeanour of the expert witness between the adversarial and court-appointed models for presentation of scientific evidence. Findings suggest that expert persona/demeanour continues to be a large focus area for jurors, that the CA model may be more resilient for ensuring greater focus on science, and that juror comprehension of science is somewhat better when presented via the court-appointed model. Results inform instruction of experts for giving opinion evidence as well as suggest the prudence of considering other models to improve the criminal justice system. Limitations as to the generalization of study results 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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".