Expert evidence or expert decisions? Measuring the impact of expert evidence on criminal proceedings outcomes in the Provincial Court of Manitoba
Bibliographic record
Abstract
Expert evidence is an integral part of the Canadian justice system, however its use comes with inherent risks. Improperly utilized, expert evidence can lead to serious injustices, such as wrongful convictions. This has been a recurring problem throughout Canadian legal history, and on which is often only identified after injustice has occurred. This study examines all reported Criminal decisions and admissibility rulings from the Provincial Court of Manitoba and compares success rates in cases without expert evidence to those where expert evidence was led to determine how the presence of expert evidence influences outcomes. It is argued that the presence of a significant positive gap between success rates with expert evidence and the base rates may be an indicator that safeguards against dubious expert evidence are ineffective. This study ultimately finds that such a positive gap does appear where the Crown leads expert evidence. The results for the defense are less clear. It is argued that this pattern is concerning, that further investigation into this needs to be done, and that existing safeguards need to be strengthened.
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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 | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".