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Record W2896196988 · doi:10.1177/1476993x18791425

Military Forces in Judaea 6–130 ce: The <i>status quaestionis</i> and Relevance for New Testament Studies

2018· article· en· W2896196988 on OpenAlexaff
Christopher B. Zeichmann

Bibliographic record

VenueCurrents in Biblical Research · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsEmmanuel Bible CollegeUniversity of Toronto
Fundersnot available
KeywordsInfantryClassicsHistoryRelevance (law)New TestamentJudaismMilitary historyAncient historyLawArchaeologyPolitical science

Abstract

fetched live from OpenAlex

The study of the military in the Roman provinces of Judaea is not the most accessible topic. Though the data upon which scholars rely is familiar (e.g., epigraphs, papyri, ancient historians), its study requires significant methodological deviations from biblical studies. This article summarizes key points relevant for scholars of both Jewish antiquity and early Christianity. First, it provides a summary of recent developments in the social history of the Roman army in the Near East, attending especially to the question of the role and function of soldiers in that region. Second, this article provides a brief social history for all military units in Judaea before it was renamed Syria Palaestina in 130 ce (four legions, 14 infantry cohortes, and five cavalry alae), based on the latest discoveries. Finally, the article concludes with a section discussing two issues specific to New Testament studies: the presence of an Italian cohort in Judaea (Acts 10) and the issue of the Augustan cohort in Judaea and Batanaea (Acts 27).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0030.011
Scholarly communication0.0040.002
Open science0.0010.002
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 head0.267
GPT teacher head0.464
Teacher spread0.197 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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