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Record W2502212348 · doi:10.1057/9780230582903_4

Can the Experience of Diaspora Judaism Serve as a Model for Islam in Today’s Multicultural Europe?

2008· book-chapter· en· W2502212348 on OpenAlexaff
Sander L. Gilman

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJudaismClothingIslamMulticulturalismOrnamentsDiasporaReligious studiesPolitical scienceHistorySociologyLawAncient historyPhilosophyArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.031
Scholarly communication0.0140.008
Open science0.0010.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.046
GPT teacher head0.280
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuePalgrave Macmillan UK eBooksSame topicJewish and Middle Eastern StudiesFrench-language works237,207