R v. NS: What's Fair in a Trial? The Supreme Court of Canada's Divided Decision on the Niqab in the Courtroom
Bibliographic record
Abstract
In 2008, a woman entered an Ontario courtroom to give evidence at a preliminary inquiry involving childhood sexual assault charges against her uncle and cousin. She sought to testify while wearing a niqab, a garment that conceals the entire head and face, leaving only an opening for the eyes. The court was asked to decide the novel question whether it could accommodate the Muslim veil in a system of justice that provided the accused with a right to face his accuser. The Supreme Court of Canada divided three ways, with justices disagreeing deeply both about the analysis for determining whether to permit a witness to wear the niqab, and the values and interests at play in this analysis.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.019 | 0.017 |
| 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".