Without Fear or Favour? Trends and Possibilities in the Canadian Approach to Expert Human Behaviour Evidence
Bibliographic record
Abstract
In R v Lavallee and R v Mohan, the Supreme Court of Canada established a test for the admissibility of expert evidence which is somewhat different from that used in other common law jurisdictions. Over the course of several more recent decisions, the court has expressed an increasingly sceptical attitude towards expert evidence of human behaviour. Collectively, these cases have left the state of Canadian law unclear. Canadian commentators also disagree about how best to navigate a path between the Scylla of uncritical reliance on expert evidence and the Charybdis of leaving discriminatory legal reasoning undisturbed. This article describes two proposals for reforming the Canadian approach to expert evidence and suggests that only one has the potential to move expert evidence jurisprudence beyond its current impasse.
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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 | 0.065 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.027 | 0.061 |
| Scholarly communication | 0.032 | 0.016 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".