Drawing the Line Between Lay and Expert Opinion Evidence
Bibliographic record
Abstract
This article examines the vanishingly thin line between lay and expert opinion evidence in Canada. In Parts I and II, we set the stakes. Canadian trial courts have been warned by peak scientific bodies and public commissions like the Goudge Inquiry about the dangers of attorning to persuasive expert witnesses. Thus, expert evidence faces new hurdles, both substantively and procedurally. This scrutiny has inspired parties to seek refuge in the more flexible and discretionary lay opinion evidence rules. But newfound vigilance to expert opinion is invalidated if the same evidence can be admitted as lay opinion. Parts III and IV illustrate this problem as we examine three cases in which authoritative lay witnesses opined on topics requiring specialized training and expertise. Three hazards are readily apparent from this analysis: (1) the lay witnesses opined on matters in which there are established methodologies to control for unconscious bias, but did not follow these methodologies; (2) the lay witnesses – police officers – were authority figures but were not qualified as experts in the area they were opining on, and; (3) the lay opinion jurisprudence has failed to meaningfully distinguish between lay and expert opinion. In Part V we seek to fill this void by proposing a new analytic approach – Lay Opinion 2.0 – which draws on both the practical and epistemological distinction between lay and expert opinion to provide an efficient and fair test for the admission of lay opinion evidence.
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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 | 0.105 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.019 | 0.076 |
| Scholarly communication | 0.029 | 0.021 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".