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Record W1965147754 · doi:10.1163/15700631-12340057

Let Them Eat Fish: Food for the Poor in Early Rabbinic Judaism

2014· article· en· W1965147754 on OpenAlexaff
Gregg E. Gardner

Bibliographic record

VenueJournal for the Study of Judaism · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsJudaismScholarshipPovertyFoodwaysChristianityJewish studiesSociologyAntiqueJewish identityHistoryReligious studiesAnthropologyLawPhilosophyPolitical scienceAncient historyArchaeology

Abstract

fetched live from OpenAlex

Abstract Recent scholarship has shown how investigations into food and poverty contribute to our understanding of late-antique Judaism and Christianity. These areas of inquiry overlap in the study of charity, as providing food was the preeminent way to support the poor. What foods and foodways do the earliest texts of rabbinic Judaism prescribe for the poor? This article examines Tannaitic discussions of the foods that should be given as charity, reading these texts within their literary and historical contexts. I find that they prescribe a two-tiered system whereby foods for the week aim to meet the poor’s biological needs, while those for the Sabbath fulfill religious requirements. These rabbinic instructions, however, also reinforce social separation and deepen the poor’s sense of exclusion. This article contributes to scholarship on poverty and charity in late antiquity, the use of food in the construction of rabbinic identity, and the tensions that arise from establishing material requirements for religious observances.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.017
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.265
Teacher spread0.186 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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