Bibliographic record
Abstract
The management of religious and ideological diversity remains a key challenge of our time—deeply entangled with debates about the nature of liberal democracy, equality, social cohesion, minorities and nationalism, security and foreign policy. This book explores this challenge at the level of the workplace in Europe. People do not surrender their religion of belief at the gates of their workplace, nor should they be required to do so. But what are the limits of accommodating religious belief in the workplace, particularly when it clashes with other fundamental rights and freedoms? Using a comparative and socio-legal approach that emphasises the practical role of human rights, anti-discrimination law and employment protection, this book argues for an enforceable right to reasonable accommodation on the grounds of religion and belief in the workplace in Europe. In so doing, it draws on the case law of Europe’s two supranational courts, three country studies—Belgium, the Netherlands and the UK—as well as developments in the US and Canada. By offering the first book-length treatment of the issue, it will be of significance to academics, students, policy-makers, business leaders and anyone interested in a deeper understanding of the potentials and limits of European and Western inclusion, freedom and equality in a multicultural context.
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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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".