Loosening the Law's Bite: Law, Fact, and Expert Evidence in R v JA and R v NS
Bibliographic record
Abstract
Faced, in the wake of the Canadian Charter of Rights and Freedoms, with decisions that bear upon unfamiliar realms of social life, Canadian courts have turned to making factual determinations based on social science and other expert evidence. Such evidence can help litigants from marginalised groups to challenge exclusionary norms and ‘common sense’ assumptions that form part of judicial reasoning. However, litigants seeking to disrupt the legal status quo in this way face a number of challenges. While many commentators have emphasised the prohibitive cost of bringing expert evidence, this article points to a prior challenge — the need to convince the court to see the relevant issue as a fact amenable to proof in the first place. To illustrate the significance of this initial framing challenge, I examine two recent criminal cases — R v JA and R v NS — where expert evidence may have been useful but was scant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.072 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.022 | 0.025 |
| 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".