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Record W2137207226 · doi:10.1177/0008429809355118

Reconstructing the Intellectual Discourse of Ancient Yehud

2010· article· en· W2137207226 on OpenAlexaffvenue
Ehud Ben Zvi

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and language evolution
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSalientSimilarity (geometry)IdeologyTask (project management)Generative grammarGrammarPreferenceLinguisticsLiteratureHistorySociologyEpistemologyComputer sciencePhilosophyArtificial intelligenceArtPoliticsPolitical scienceLawArchaeologyMathematics

Abstract

fetched live from OpenAlex

This article deals with ways in which historians may approach the task of reconstructing the intellectual discourse of ancient Yehud, as well as the feasibility of the project as a whole. It shows, in particular, how a comparative study of implied patterns of preference that shaped two sets of works that were separate in time and differentiated by literary genre, namely the prophetic books and the Book of Chronicles, contributes to this task. This article demonstrates that, despite all their differences, both sets of works shared a common, ideological, generative grammar. Certain issues tended to be raised, particular sets of ways of approaching and answering them to be used, and conceptually similar metaphors, and comparable fears and hopes, to shape the imagination of the literati for whom and by whom these books, with all their differences, were composed. This article then discusses the historical implications of this basically shared discourse and the ways in which a background of general discursive similarity makes instances of dissimilarity even more salient. A few examples of the latter are raised.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0080.019
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0010.002
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 head0.074
GPT teacher head0.353
Teacher spread0.279 · 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 designQualitative
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
Published2010
Admission routes2
Has abstractyes

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Same venueStudies in Religion/Sciences ReligieusesSame topicLinguistics and language evolutionFrench-language works237,207