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Record W3005573822 · doi:10.3167/hrrh.2019.450304

Yonatan Ratosh's "Cultural Entrepreneurship" and the Invention of "Hebrew" Nationalism

2019· article· en· W3005573822 on OpenAlexvenueno aff
Amotz Giladi

Bibliographic record

VenueHistorical Reflections/Réflexions Historiques · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHebrewNationalismHebrewsZionismCivilizationIdeologyLiteratureJewish identitySociologyPolitical scienceLawPhilosophyJudaismTheologyArtPolitics

Abstract

fetched live from OpenAlex

Israeli poet Yonatan Ratosh was the leader of the Young Hebrews, a nationalist group active from the 1940s to the 1970s. Despite his opposition to Zionism and his aspiration to revive the ancient Hebrews’ premonotheistic civilization, Ratosh shared Zionism’s ambition to elaborate a new Israeli identity. One prominent act of this mission involved enlarging the literary corpus in Hebrew through translation. Although initially a means of income, for Ratosh translation increasingly came to be a way to express his ideological position and his self-image as an intellectual. Thus, Ratosh provides an example of how developing a national identity can coincide with appropriating foreign literature. With his regular exhortations that Hebrew readers attain knowledge of foreign cultures, Ratosh did not intend to promote cosmopolitanism. Rather, he considered these endeavors as ultimately reinforcing a “Hebrew” identity.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.009
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.324
Teacher spread0.281 · 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
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

Citations3
Published2019
Admission routes1
Has abstractyes

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