Dubbed Jewish Literature: Multilingualism, Translation, and Hopelessness in Twentieth-Century Hebrew and Yiddish Writing
Bibliographic record
Abstract
Early twentieth-century Hebrew and Yiddish writers strived to create the kind of psychologically nuanced narrative worlds they had encountered in European literature. However, writing in marginal languages, they faced a paradox: achieving psychological realism when the language of narration differs from the spoken language(s) of the story world. My dissertation examines how Yiddish and Hebrew writers of the twentieth century bridged the persistent gap between the spoken language of the story world and the written language on the page. I focus on the strategy of “literary dubbing” in the representation of speech. Dubbed literature refers to prose written through a translational process where authors render characters' speech in a narrational language distinct from the spoken diegetic languages. My analysis explores literary dubbing in texts by Uri Nissan Gnessin, Dovid Bergelson, Hava Shapiro, Rivke Rus, S.L. Blank, and Aaron Zeitlin. This research investigates the complex relationships across Jewish and non-Jewish languages, native and foreign languages. Tracing this evolution from the multilingual lives of 'at-home' Hebrew and Yiddish writers (and their characters) through the profound shift reflecting Jewish immigrant hardship, it reveals the deep alienation and hopelessness caused by a lack of linguistic belonging. I investigate how dubbed literature provides more than a technical solution to literary representation of multilingualism. Instead, I argue, it is a powerful testament to the unresolved tension of linguistic identity within Jewish modernism, faced by authors striving to represent a multifaceted Jewish experience in a world demanding linguistic singularity. Studying both well-known texts and introducing a previously unstudied corpus, I argue that Jewish writers of that time, both Hebrew and Yiddish, across the world participated in active attempts to create a monolingual Jewish literature to elevate their language, suppressing other languages in the process. As they grappled with the tension between their desired language and their everyday language, they exposed the impossibility of multilingualism and their state of linguistic homelessness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".