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Record W7073944111

Waters of the Exodus Jewish experiences with water in Ptolemaic and Roman Egypt

2016· article· en· W7073944111 on OpenAlexaff

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsUniversity of Toronto
FundersPrinceton University
KeywordsJudaismWorshipAcknowledgementMateriality (auditing)NarrativePower (physics)Honour
DOInot available

Abstract

fetched live from OpenAlex

This study examines how the fluvial environment shaped the writing of Jewish narratives in Ptolemaic and Roman Egypt (300 BCE – 115 CE). It focuses on four texts that narrate the Exodus story and analyzes them in terms of how water in Egypt is described and how elements of the environment—such as the Nile or the Red Sea—are characterized in their retellings. I argue that the natural environment informed Jewish writings through the incorporation of new fluvial terminology, development of different conceptions of water, and the adoption of positive attitudes towards the environment. These features, found specifically in texts composed in Egypt, demonstrate the power of the environment to shape a foundational Jewish narrative. Previous studies on the Jews of Egypt have examined their lives in political, social, or economic terms, with little acknowledgement of the physical environment and its role in daily and religious life. By focusing on water, this work traces the emergence of distinct practices developed in response to the environment, such as the location of places of worship and emerging employment opportunities. Such characteristics distinguish the communities of Egypt from both other Jews and non-Jews. Additionally, the project speaks to larger trends in the field of Biblical Studies that focus on the materiality of everyday life.

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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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