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Record W2045230433 · doi:10.3917/eger.275.0423

Le Léviathan de Joseph Roth. La problématique juive et le shtetl

2015· article· fr· W2045230433 on OpenAlexaboutno aff
Astrid Starck-Adler

Bibliographic record

VenueÉtudes Germaniques · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNovellaBeautyJudaismArtArt historyHistoryLiteratureArchaeology

Abstract

fetched live from OpenAlex

The Jewish motif plays a major role in Joseph Roth’s works. In The Leviathan, a strange and complex novella written during his Parisian exile and the last years of his life, the author returns to the theme of the Ostjuden: the plot is set in a Jewish-Russian townlet (shtetl). The “hero”, Nissen Piczenik, is a coral merchant. A very interesting osmosis occurs between the man and his corals. In his eyes they are living animals which, put around the necks of peasant women, instil life, beauty and love. Through folkloristic or midrashic motives – the coral’s apotropaic power or the sea monster that watches the oceans until the coming of the Messiah – and through alchemy – water and fire – Joseph Roth leads the reader into a man’s inner world in which he is stuck. Comes the day when, like the corals fished in cold or warm waters and brought to the mainland, an “oceanic feeling” wells up within him and awakes his desire to know their world. A sailor takes him onto his battleship, and from then on, he lives between two worlds, the world of delusion and the world of reality: he lets himself be seduced by the false coral his concurrent offers him and mixes it with the real one. He replaces, thanks to drunkenness, the mainland by the rough sea, until he decides to burn the false coral and to keep only the real one that comes from the water. Then he leaves for Canada and embarks on the “Phenix”. The ship casts away and Nissen lets himself sink into the abyss of the ocean, a coral amongst corals.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.046
GPT teacher head0.317
Teacher spread0.271 · 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.

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
Published2015
Admission routes1
Has abstractyes

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