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Record W346691273

GOOD FENCES DO NOT NECESSARILY MAKE GOOD NEIGHBORS: JEWS AND JUDAISM IN CANADA'S SCHOOLS AND UNIVERSITIES

2016· article· en· W346691273 on OpenAlexaboutno aff
Michael Brown

Bibliographic record

VenueJewish political studies review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsJudaismComityLegitimacySociologyState (computer science)MulticulturalismLawPoliticsSeparation of church and stateWorld War IILoyaltyMainstreamJewish stateReligious educationReligious studiesPolitical scienceTheologyJurisdiction
DOInot available

Abstract

fetched live from OpenAlex

In the post-World War II years, strict of church and state, especially with regard to education, has been viewed as an essential ingredient of social comity in the United States. In Canada, however, that has not been so. In fact, there, religion and education have been intimately connected since colonial times, and the role of religion in the schools has constitutional sanction. In the years before World War II, the outsider status of Jews and Judaism in schools and universities was demeaning to them. Ultimately it served to reinforce group loyalty, but Jewish educational institutions did not emerge. The much less parochial and eventually multicultural environment that developed begin ning in the 1950s allowed Jews to become part of the mainstream. Ethnic legitimacy, however, fostered the development of a very successful system of all-day Jewish schools and of programs of Jewish Studies at universities across the country. This essay dis cusses these developments and suggests explanations for the seeming paradox. The putative high wall of separation said to divide religion from state in the United States is usually considered to be one of the important devices that ensure the proper functioning of American democracy. At some historical moments the wall has, in Jewish Political Studies Review 11:3-4 (Fall 1999)

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.321
Teacher spread0.287 · 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 teacher head, not a consensus.

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

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

Citations1
Published2016
Admission routes1
Has abstractyes

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