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Record W4389191234 · doi:10.1111/rec3.12479

Cannibal Maria in the Siege of Jerusalem: New approaches

2023· article· en· W4389191234 on OpenAlexaff
Mo Pareles

Bibliographic record

VenueReligion Compass · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsJosephusJudaismHebrewSiegeJewish literatureClassicsLegendLiteratureHistoryJewish studiesArtTheologyPhilosophyAncient history

Abstract

fetched live from OpenAlex

Abstract This essay traces the far‐reaching legend of Maria/Miriam of Bethezuba, sometimes called Mary, Marie, or Marion, a starving Jewish woman who (according to Flavius Josephus's The Jewish War ) ate her own baby during the 70 CE Roman Siege of Jerusalem. This episode of maternal infanticide and cannibalism under occupation is the culmination of Biblical curses and prophecies, a complicated reference to the Eucharist, and an emblem of Jewish (women's) suffering and culpability across time. It is also a key to Jewish‐Christian arguments about futurity and the writing of history. Scholarly developments in the past decade prompt a new look at this episode. These include research on Greek, Latin, Hebrew, Arabic and other translations of Josephus that demonstrate complex relationships among Jewish, Christian, and Muslim readerships and modern English translations of key Hebrew, Arabic, Ge'ez (Ethiopic), and Middle English versions of the story. This essay provides a brief literary and theological history of Maria's story informed by the new scholarship, with particular attention to medieval Jewish‐Christian relations, and suggests additional directions for research.

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)0.003
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0120.024
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.118
GPT teacher head0.263
Teacher spread0.145 · 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
Published2023
Admission routes1
Has abstractyes

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