Reasonable Accommodation for Religious Minorities: A Promising Concept for European Antidiscrimination Law?
Bibliographic record
Abstract
In both the United States and Canada, the concept of reasonable accommodation first emerged in equality law as a means of handling religious diversity. It was then applied to other grounds of discrimination, most notably disability. In the European Union, the evolution of antidiscrimination law is following a different path: a duty of reasonable accommodation was for the first time established by the 2000 Employment Equality Directive (or Directive 2000/78/EC) but only with respect to disability. Nonetheless, the question whether a right to reasonable accommodation can be derived from the prohibition of discrimination based on religion laid down by the same Directive, or, alternatively, whether such right should be recognized in future European legislation is becoming increasingly salient.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".