The Judicial Admission of Faulty Scientific Expert Evidence Informing Wrongful Convictions
Bibliographic record
Abstract
The failure of a judge to properly conduct a voir dire to ensure an expert is sufficiently qualified to give evidence in a particular area may give rise to a wrongful conviction. Considering Recommendation 130 from the Goudge Inquiry, that “trial judges should be vigilant in exercising their gatekeeping role with respect to the admissibility of [expert] evidence”, it is essential to examine recent shortcomings of judicial vigilance in admitting expert evidence and to consider how to remedy similar errors in future cases.\nThe major shortcoming of judicial gatekeepers in admitting expert evidence is the improper application of case law regarding the test in R v Mohan and consideration of the factors in Daubert. Systemic patterns of judicial error are demonstrated through the repeated admission of expert evidence given by Doctors Charles Smith and Gideon Koren. By analyzing the flaws in Dr. Smith’s admitted testimony in the context of the Mohan test, and the misapplication of the Daubert factors to Dr. Koren’s evidence, it is clear that the gatekeeper’s acceptance of unqualified or underqualified expert evidence can be avoided by holding fulsome voire dires and adopting techniques utilized in other jurisdictions to reduce the potential of wrongful convictions.
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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 | 0.055 | 0.275 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.011 |
| 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".