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Record W4381144876 · doi:10.1080/09018328.2023.2222037

“Your Gates”—Evoking a Landscape of Fortified Cities in Deuteronomy: Meanings, Implications, and Comparative Considerations with Other Constructions of the Israelite Past

2023· article· en· W4381144876 on OpenAlexaff
Ehud Ben Zvi

Bibliographic record

VenueScandinavian Journal of the Old Testament · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolityIdeologyHistoriographyHistoryLiteratureSpace (punctuation)AestheticsSociologyLawPhilosophyLinguisticsPoliticsArtArchaeologyPolitical science

Abstract

fetched live from OpenAlex

This article is about constructions of internal territorial space and its ideological implications. It discusses the lay of the land of the Israelite society that the literati “saw” when reading Deuteronomy. It focuses on the horizontal, non-hierarchical arrangement of an array of cities that so strongly characterizes the book. Then it addresses what the preference for this array suggests in terms of the polity that Deuteronomy conjures and how this preference relates to both central ideological tenets of the book and additional features of its imagined polity. Neither the imaginary landscape of the Israelite polity nor the polity itself evoked by Deuteronomy were consistent with those evoked by any of the “historiographical” works in Yehud. As a result, its literati had to assume either that such a landscape was never actually fulfilled in any period of the past or try to conform Deuteronomy’s landscape to other past-shaping, ideological texts (and related memories) that existed in their core repertoire, or both, in a complementary, balancing way.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.034
Scholarly communication0.0080.004
Open science0.0000.003
Research integrity0.0010.002
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 head0.068
GPT teacher head0.263
Teacher spread0.196 · 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 designTheoretical or conceptual
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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