Bibliographic record
Abstract
This article presents the argument that our understanding of the nature of the relationship between modern constitutionalism and religious difference has suffered with the success of the story of legal tolerance and multiculturalism. Taking up the Canadian case, in which the conventional narrative of legal multiculturalism has such purchase, this piece asks how the interaction of law and religion - and, in particular, the practices of legal tolerance - would look if we sought in earnest to understand law as a component, rather than a curator, of cultural diversity in modern liberal societies. Understanding the law as itself a cultural form forces us to think about the interaction of law and religion as an instance of cross-cultural encounter. Drawing from theoretical accounts of cross-cultural encounter and philosophical literature about the nature of toleration, and paying close attention to the shape of Canadian constitutional doctrine on religious freedom (law’s rules of cross-cultural engagement), this paper suggests that legal toleration is far less accommodative and far more assimilative than the conventional narrative lets on. Influential alternative theoretical accounts ultimately reproduce this dynamic because they similarly obscure the role of culture on both sides of the encounter of law and religion. Indeed, owing to the particular features of the culture of law’s rule, even the more thickly cultural "solutions" proposed in dialogic theory ultimately fail. In the end, this article exposes the very real cultural limits of legal tolerance.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.070 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".