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Record W4413341685 · doi:10.1093/jts/flaf046

Hedged in, Hunted, Haunted, Hiding: Divine Presence and Absence in the Dialogues in Job

2025· article· en· W4413341685 on OpenAlexaff
Brittany N. Melton, Katharine J. Dell

Bibliographic record

VenueThe Journal of Theological Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsRegent College
Fundersnot available
KeywordsHistory

Abstract

fetched live from OpenAlex

Abstract This article examines the complex conceptualization of divine presence and absence in the dialogue sections in the book of Job through seven categories, which exemplify a disjointed portrayal of God and the dissonance between Job’s perspective and that of the friends. The metaphors Job uses for divine presence are more dynamic: hedging, hunting, haunting, wounding, creating, and destroying. The friends reuse these same metaphors but repurpose them in the service of theological explanations, rarely speaking out of personal experience. Job alone voices the inexplicability that God is ignoring him and hiding his face, or simply inactive, whilst for the friends, Job’s experience of God’s absence is justifiable. Job’s disjointed portrayal of divine oppressive presence alongside absence can be explained by its rhetorical function, which is to demonstrate the depth of his suffering in order to persuade either God or the friends to alleviate it. Therefore, in addition to illuminating the multi-layered nature of comprehending conceptualizations of divine presence and absence in Job, broader implications are drawn for interpreting portrayals of God in the Hebrew Bible, in the recognition that the rhetorical force and circumstances of the speaker impact the reader’s evaluation and expression of God’s presence and absence.

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)0.007
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: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.053
Scholarly communication0.0100.006
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.083
GPT teacher head0.310
Teacher spread0.227 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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