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Jewish Cultural Heritage, Minority Agency, and the State: Introduction

2025· article· en· W4416901976 on OpenAlexaff
Miranda Crowdus, Yulia Egorova, Samuel Sami Everett

Bibliographic record

VenueEthnoscripts · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsConcordia University
FundersDeutsche ForschungsgemeinschaftEuropean Commission
KeywordsJudaismScholarshipNegotiationEthnographyPerspective (graphical)Cultural heritageJewish state

Abstract

fetched live from OpenAlex

Using an interdisciplinary perspective at the intersections of anthropology, Jewish Studies, and critical academic scholarship of heritage, this special issue presents ethnographic examples to explore the relationship between minority groups and the state through the prism of representations of Jewish cultural heritage in the European public sphere. On an empirical level, the articles focus on personal, community-led, and wider public discussions of the way Jewish experience and histories of migration have been (or should be) represented in museums and historical sites, in musical productions and open-air displays, at sites of restitution and in virtual spaces. In this introductory article we summarise the main points of each contribution and some of their connected themes. We then briefly discuss the articles we brought together and outline the main matters of theoretical concern they raise. Key are the aspirations that members of Jewish communities have in negotiating representations of Jewish heritage in Europe and the agentive capacity that diverse Jewish publics, including individual artists and professionals, demonstrate in shaping these representations to achieve, disrupt, or suspend state-sponsored consensus about the preservation of minority heritage.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.010
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.058
GPT teacher head0.256
Teacher spread0.197 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2025
Admission routes1
Has abstractyes

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