Bibliographic record
Abstract
Abstract The Free Exercise Clause of the U.S. First Amendment has been conceptualized by the Supreme Court and many commentators in all- or-nothing terms. The Court has tended to either apply “strict scrutiny” to burdens on religious exercise—in which case the government’s justi*cations for imposing the burden are rigorously reviewed––or instead, as of late, to apply a minimalist test that asks only whether government burdens on religion are motivated by antireligious sentiment. As the following materials and notes illustrate, other jurisdictions have tried to chart more nuanced middle-ground approaches. Whether balancing tests such as Canada’s, discussed below, are workable––there or more generally— is for readers to decide. Note that Canada, unlike the United States, has no explicit non establishment provision in its Charter. And yet, as the materials below suggest, Canada has incorporated many of the principles of religious equality on which much of the U.S. Establishment Clause jurisprudence has been built on. Perhaps Canada’s textual protection for multiculturalism has been an adequate vehicle in this regard. After reading the materials, consider whether the Fourteenth Amendment’s Equal Protection Clause could or should do more work in U.S. disputes implicating religious equality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".