Bibliographic record
Abstract
In 2010, when The New Yorker published a list of twenty writers under the age of forty who were “key to their generation,” it included five Jewish-identified writers, two of whom—American Gary Shteyngart and Canadian David Bezmozgis—were Soviet-born. This publicity came after nearly a decade of English-language literary output by Soviet-born writers of all genders in North America. Soviet-Born: The Afterlives of Migration in Jewish American Fiction traces the impact of these now numerous authors—among others, David Bezmozgis, Boris Fishman, Keith Gessen, Sana Krasikov, Ellen Litman, Gary Shteyngart, Anya Ulinich, and Lara Vapnyar—on major coordinates of the Jewish American imaginary. Entering an immigrant, Soviet-born standpoint creates an alternative and sometimes complementary pattern of how the Eastern and Central European past and present resonate with American Jewishness. The novels, short stories, and graphic novels considered here often stage strikingly fresh variations on key older themes, including cultural geography, the memory of World War II and the Holocaust, communism, gender and sexuality, genealogy, and finally, migration. Soviet-Born demonstrates how these diasporic writers, with their critical stance toward identity categories, open up the field of what is canonically Jewish American to broader contemporary debates. This book is also freely available online as an open-access digital edition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".