Kome! Beve! Bive: Eat! Drink! Live! How Cookbooks Recreated Sefarad in the U.S. Diaspora and Inspired a Communal Identity
Bibliographic record
Abstract
This project is a historical narrative of firsts in U.S. Sephardi culinary texts, both recipes and cookbooks, from 1805 to 1994 and chronicles Sephardi migration, identity, and values. By default, this text is a history of Sephardic women, as they have been the primary cooks and transmitters of Judeo-Spanish tradition. This project is also an exploration of how Judeo-Spanish and Sephardi identity have been understood, preserved, and transmitted since exile from the Iberian Peninsula in 1492. In six sequential chapters, this work examines how the production and consumption of food has intersected with constructions of gender, race, class, and national origin to negotiate and reinterpret citizenship to the American and Jewish nations. By defining Sephardi cuisine in the U.S. and outlining a canon of essential recipes, this text illuminates the link between Medieval Judeo-Spanish recipes and their recreation in the modern diaspora. Specifically, this study examines Sephardi Jewry’s relationships to the nation state and the Ashkenazi Jewish majority and the creation of a shared and cohesive pan-Sephardi identity in the U.S. Drawing on communal and academic archives, personal correspondence and diaries, oral histories and cookbook author interviews, census records, and Jewish community studies, a complex account of the Sephardi experience in the U.S. emerges, one that is both unique and also universal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".