Faithful Translations?: Cross-Cultural Communication in Canadian Religious Freedom Litigation
Bibliographic record
Abstract
In three religious freedom cases pursued to the Supreme Court of Canada—Amselem, Multani, and Huterrian Brethren of Wilson Colony—religious freedom claimants engaged in litigation over a religious practice particular to their group. Some have argued that cases like these can be seen as cross-cultural encounters. How did the religious freedom claimants seek to make their practices—the succah, the kirpan, and the prohibition on being photographed—understood to the courts? And how did the courts respond to these claims? In this article, I draw out two central values from the literature on crosscultural communication: respect and self-awareness. I then use these values as lenses through which to view participant narratives collected in a qualitative study of litigants, lawyers, and an expert witness. I argue that courts are more likely to achieve a fuller understanding of minority religious practices when they are faithful to the values of respect and self-awareness. This, in turn, can strengthen their proportionality analyses. Of the three cases, the Supreme Court in Multani came the closest to realizing this ideal. The majority in Wilson Colony marked a low point in this regard, failing to fully appreciate the litigants’ commitment to their collectivist worldview. Amselem was something of a middle ground, where the Court deliberately preferred to engage with a thinner account of the religious practice.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.061 | 0.026 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".