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Record W1589270080

A Review of Klára Móricz's Jewish Identities: Nationalism, Racism, and Utopianism in Twentieth-Century Music

2013· review· en· W1589270080 on OpenAlexaff
Ana Angelovska

Bibliographic record

Venuenot available
Typereview
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsJudaismNationalismHaskalahRacismJewish identityMythologyEssentialismReligious studiesIdentity (music)Jewish musicZionismEthnic groupLiteratureHistorySociologyGender studiesAestheticsJewish studiesArtAnthropologyPhilosophyPolitical scienceLawPoliticsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Klara Moricz explores the question of the ethnic identity of composers of Jewish origin in Jewish Identities: Nationalism, Racism, and Utopianism in Twentieth-Century Music. The material in this 436-page book is organized in three parts, “Jewish Nationalism a la Russe : The Society for Jewish Folk Music,” “Man’s Most Dangerous Myth: Ernest Bloch and Racial Thought,” and “Utopians/Dystopias: Arnold Schoenberg’s Spiritual Judaism.” The three main ideas running through Moricz’s book are a comparison of the differences between Jews in Russia, Switzerland, France, Austria, Germany, and the United States; the involvement of Jewish composers in Jewish culture and as part of an international society; and how these Jewish composers were affected by the new social and world events that arose at the beginning of the twentieth century. Moricz believes “the study of Jewish identities in professional music, with an emphasis on their complex, often conflicting nature, [is] something much ignored in essentialist studies of identity,” (18) thus providing the scholarly community with an in-depth study of a topic not well explored.

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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.009
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.078
GPT teacher head0.303
Teacher spread0.224 · 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
GenreReview

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
Published2013
Admission routes1
Has abstractyes

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Same topicMusicology and Musical AnalysisFrench-language works237,207