Bibliographic record
Abstract
This article challenges the conventional understanding of the separation of church and state by arguing that there is no analytical or constitutional problem with using religious law for the purpose of constitutional interpretation. We situate our arguments within the context of the broader debate on the use of foreign law in constitutional interpretation, and the more recent controversy surrounding the proposed bans on the use of religious law in US state courts. By examining the arguments for and against the use of foreign law, we show how they equally apply to the use of religious law. More importantly, we conclude that differences between foreign law and religious law are, at best, differences of degree rather than kind, and thus do not militate against the use of religious law in constitutional interpretation. This article demonstrates that religious law can be used, and in fact, has already been used by the Supreme Court for four limited purposes, none of which, we argue, offends the principles underlying the Establishment Clause. The ultimate import of our claim is not that religious law should be used by courts, but that recognizing its potential as a source in constitutional interpretation should result in a deeper and more careful engagement with the possibilities it generates.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| 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".