Combatting Stereotyping & Facilitating Justice: McLachlin's Vision for the Law of Evidence
Bibliographic record
Abstract
Beverley McLachlin is the architect of a flexible, socially conscious and principled approach to evidence admissibility in Canada. Her jurisprudence has infused the law of evidence with tools that enable it to adapt to new situations, to be aware of and reflect concerns for systemic issues all with an eye to ensuring it can fulfill its regulatory purpose of facilitating justice. I call this the McLachlin principle. This chapter explores the foundations of that approach in two early McLachlin decisions: R v Khan; R v Seaboyer; and then, as Chief Justice, in Mitchell v MNR where she set out, for the first time in a Supreme Court decision, a theory of evidence admissibility. After examining this evidence trilogy, the chapter will consider the application of the McLachlin principle in the context of defence applications to limit cross-examination of an accused on their prior criminal record under R v Corbett. Section 12(1) of the Canada Evidence Act permits all witnesses, including an accused, to be cross-examined on their criminal record and our common law has, for the most part uncritically, accepted that a criminal record is relevant to a witness’s credibility and whether they are prepared to abide by their oath or affirmation. In Corbett, the Supreme Court of Canada upheld the constitutionality of section 12(1) by reading into the provision a judicial discretion to prohibit or limit cross-examination on a prior record. Corbett was decided in 1988 and since then we have become more aware of the existence and manifestations of systemic racism, particularly as it relates to Indigenous and Black communities and the criminal justice system. Chief Justice McLachlin recognized this social reality in both Sauve v Canada (Chief Electoral Officer) and R v Williams. Despite this consciousness, little, if any, attention has been given in our trial and appellate courts to how social conditions and bias are relevant in thinking about admissibility under Corbett. Enter the McLachlin principle. The chapter examines how it can be used to impact Corbett applications and stimulate future consideration of how evidence law can adapt to better facilitate justice in cases involving Indigenous and racialized participants.
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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 | 0.063 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.020 | 0.184 |
| Scholarly communication | 0.042 | 0.038 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.024 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 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".