Bibliographic record
Abstract
This paper analyzes Canada’s common law as it currently stands regarding expert evidence and key inquiries and reports on expert evidence and wrongful convictions done in Canada and on forensic science. The analysis will demonstrate how Canada’s laws and practices have not gone far enough to ensure expert evidence is reliable in order to protect innocent citizens from wrongful conviction. I propose that to truly safeguard against the admission of improper expert evidence in trials Canada must (1) heighten the standard expert evidence must meet to be considered reliable and increase a judge’s role as gatekeeper (2) foster a system of peer-reviewed research, training, accreditation, and accountability in forensic science disciplines in Canada, and (3) ensure that all legal actors (i.e., police, lawyers, and judges) receive continued training on best forensic science practices and their limits and have free access to information and education on forensic science disciplines when needed. Systemic changes in forensic science disciplines in Canada, continued education in forensic sciences for legal actors, and changes in the law of reliability of evidence are necessary to prevent improper expert evidence from continuing to contribute to wrongful convictions. This paper concludes by with a case study evaluating how the proposed systemic changes could have made a difference in a real-world case where expert evidence was flawed.
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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 | 0.113 | 0.384 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.022 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".