Whether Angel or Devil: Law's Knowing and Unknowing of Veiled Muslim Women in the Case of R v. N.S.
Bibliographic record
Abstract
In this thesis, I examine the recent Canadian Supreme Court case of R v. N.S., wherein a woman sought to have her right to wear a face-veil while testifying affirmed by the Court, under her Charter rights to freedom of religion.This case involves questions of religious freedom and fair trial rights, the rights and roles of sexual assault victims and witnesses, and the bounds of accommodation and toleration.I argue that the discourse in the case, from legal actors such as counsel for N.S. and the accuseds, Crown attorneys, and the justices of the Court, operates as a lens through which conceptions of identity, otherness, nationhood, and veiled Muslim women become refracted and known.Ultimately, I claim that these forms of knowledge not only construct and reinscribe binary modes of thinking about veiled Muslim women, but also allow for and result in the eliding of N.S.'s subjectivity and agency.iii Acknowledgments "Whoever does not thank people, does not thank God". Prophet Muhammad ﷺFirstly, many thanks to my supervisor, Professor Christiane Wilke.I could not have achieved this without your massive help and encouragement.Thank you for giving me the tools and the confidence to first determine, and then to
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.070 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.018 |
| 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".