Can the Experience of Diaspora Judaism Serve as a Model for Islam in Today’s Multicultural Europe?
Bibliographic record
Abstract
Two moments in modern history: a religious community in France is banned from wearing distinctive clothing in public schools as it is seen as an egregious violation of secular society; a religious community in Switzerland is forbidden from ritually slaughtering animals as such slaughter is seen as a cruel and unnatural act. These acts take place more than a hundred years apart: the former recently in France, the latter more than a century ago in Switzerland (where the prohibition against ritual slaughter still stands). But who are these religious communities? In France (among other countries) the order banning ostentatious religious clothing and ornaments in schools and other public institutions impacts as much on religious Jewish men who cover their heads (and perhaps even religious Jewish married women who cover their hair) as it does the evident target group, Muslim women. (The law is written in such a politically correct way as also to ban the ostentatious wearing of a cross: ‘Piene, you can’t come into school carrying that six-foot-high cross on your back. You will have to simply leave it in the hall.’) In Switzerland, even today the prohibition against kosher Jewish slaughter also covers the slaughter of meat by Muslims who follow the ritual that results in Halal meat. These prohibitions impact on Jews and Muslims in oddly similar ways when Western responses to ‘slaughter’ are measured. Very different is how the meat is used: whether in ‘traditional’ dishes or in a ‘Big Mac’. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".