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Record W2770736285 · doi:10.1163/15685179-12341446

Reading God’s Will? Function and Status of Oracle Interpreters in Ancient Jewish and Greek Texts

2017· article· en· W2770736285 on OpenAlexaff
Hanna Tervanotko

Bibliographic record

VenueDead Sea Discoveries · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Linguistic Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDead Sea ScrollsJudaismInterpreterJewish studiesBiblical studiesHebrew BibleReading (process)Jewish literatureIdentity (music)MandateBiblical languagesOracleLiteratureClassicsAncient GreekHistoryLinguisticsPhilosophyArtComputer scienceLawArchaeologyPolitical scienceAesthetics

Abstract

fetched live from OpenAlex

Abstract There is a rising scholarly consensus that consulting the divine will did not altogether cease in the Second Temple period. Rather, it took different forms, and one was consulting the divine will via existing texts. Meanwhile, the identity of such interpreters remains unclear. This paper explores the possible identities of interpreters by comparing the figures that interpret Jewish oracles with the chresmologoi that appear in ancient Greek compositions. Such a comparison provides new insights into the divinatory use of written oracles. The interpreters of the Jewish and Greek texts operated at least partly in similar ways. While their methods of interrogating the oracles are somewhat alike, Jewish interpreters enjoyed a status similar to that of prophetic figures, whereas Greek interpreters operated more independently and without a similarly evident divine mandate.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.012
Scholarly communication0.0040.002
Open science0.0000.002
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 head0.020
GPT teacher head0.294
Teacher spread0.274 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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