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Record W2039858394 · doi:10.1177/1476993x14532750

An Almanac of Tobit Studies: 2000-2014

2014· article· en· W2039858394 on OpenAlexaff
Andrew B. Perrin

Bibliographic record

VenueCurrents in Biblical Research · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsTrinity Western UniversityWestern University
Fundersnot available
KeywordsTobit modelApocryphaNarrativeIntertextualityHebrew BibleHistoryBiblical studiesGeographyLiteratureClassicsArtArchaeologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Arguably the most influential moments in the entire history of Tobit studies were the acquisition of the Qumran cave four Aramaic and Hebrew Tobit fragments in 1952 and their eventual publication in 1995. In light of these events, this article surveys the major advancements in resources and research on the book of Tobit since the turn of the millennium. The present survey establishes the status quaestionis on matters of Tobit’s compositional origins (i.e., language, date, and provenance) as it has emerged in several recent articles, monographs, and commentaries. Following the treatment of background issues, three thematic sections capture the major trends in recent Tobit studies. These include: (1) theories of Tobit’s scribal transmission and related text-critical issues, (2) questions of source material and intertextuality in Tobit’s composition and reception, and (3) a reappraisal of central narrative-theological features in Tobit (i.e., marriage and family, perspectives on burial, and the functions of food) and their potential insight into the book’s socio-historical contexts in ancient Judaism. The study concludes with some brief recommendations and open-ended questions for future research on the book of Tobit.

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)0.019
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 score0.016
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.023
Science and technology studies0.0060.009
Scholarly communication0.0120.009
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.306
GPT teacher head0.472
Teacher spread0.166 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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