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Record W2563697608 · doi:10.1163/15685330-12341256

Subversive Boundary Drawing in Jonah: The Variation of אשר and שׁ as Literary Code-Switching

2016· article· en· W2563697608 on OpenAlexaff
Robert D. Holmstedt, Alexander T. Kirk

Bibliographic record

VenueVetus Testamentum · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSubversionHebrewHebrew BibleLiteratureIdentity (music)PhoenicianVariation (astronomy)Biblical HebrewReading (process)Biblical studiesNarrativeIntertextualityCode (set theory)LinguisticsHistoryPhilosophyArtAestheticsLawComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This study presents literary code-switching as the best explanation for the variation of אשׁר and שׁ in the book of Jonah. The use of Hebrew אשׁר and a Phoenician-based שׁ in the world of the narrative is used both to create and destroy identity boundaries. The switch between אשׁר and שׁ is the central linguistic strategy supporting the subversion of the intended audience’s natural reading sympathy (initially with Jonah) and theology (an ethnically exclusive Yahwism). Jonah’s use of שׁ represents a linguistic flight from his Hebrew identity, while the sailors’s and Ninevite king’s use of אשׁר represents their recognition of Yhwh as a god worthy of devotion. And Yhwh’s use of both אשׁר and שׁ signals the author’s view that Yhwh does not exclusively belong to (or care for) the Hebrew people.

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)0.013
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 score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.035
Scholarly communication0.0110.007
Open science0.0020.006
Research integrity0.0020.004
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 head0.016
GPT teacher head0.229
Teacher spread0.212 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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