Solidarity in Diversity? State Responses to Religious Diversity in Liberal and Non-Liberal Perspectives
Bibliographic record
Abstract
Abstract This Article introduces the German Law Journal’s Special Issue on “Solidarity in Diversity? State Responses to Religious Diversity in Liberal and Non-Liberal Perspectives”. The major countries in comparative focus are Germany and Singapore, both self-avowedly secular countries that face the challenge of religious diversity: Singapore, from inception, and Germany, through more recent developments. A key issue the Article raises concerns liberal approaches towards regulating religion; it argues that the liberal model, taking Germany as an example, may serve as a productive starting point for comparative analysis, as the liberal focus on individual religious freedom impacts managing religious diversity, shapes national cultural identity, models of secularism and social solidarity. This is compared with non-liberal approaches, as exemplified in Singapore practice, where a more communitarian outlook underpin more interventionist approaches whereby public interests and the common good tend to be prioritized over individual freedom. The comparative angles offered in this Special Issue is furthermore buttressed in several articles in this Special Issue that make comparisons to other jurisdictions-United States and Canada. This introductory Article offers a brief overview to the various contributions to this Special Issue and identifies unifying themes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".