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Record W2944819920 · doi:10.1080/00437956.2019.1611526

A relevance-theoretical perspective on the question of why Jesus never wrote a book

2019· article· en· W2944819920 on OpenAlexaff
Patrick Duffley

Bibliographic record

VenueWORD · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsReverenceTorahRelevance (law)PhilosophyMeaning (existential)JudaismUtteranceCharacter (mathematics)Perspective (graphical)Natural (archaeology)LiteratureReading (process)EpistemologyLinguisticsTheologyHistoryLawArt

Abstract

fetched live from OpenAlex

Many reasons have been given for why Jesus never wrote anything. Some have argued that this was because he, or his audience, was illiterate; some that it was because Jewish rabbis only gave oral teaching; others that it was to avoid the idolatrizing of a divinely authored book. Moreover, given the deep reverence among the Jews for the written word found in the Torah, and the fact that Jesus claimed for himself the same authority as that upon which the Torah was based, it would seem legitimate to ask the question: why did he not seek to displace the Torah by a book of an equivalent or greater authority, like that claimed for the Koran in the Islamic tradition? The goal of this paper will be to bring to the table some linguistic arguments for why Jesus never committed any of his teachings to writing based on certain characteristics of natural language that have been highlighted by Relevance Theory, namely the underspecified nature of linguistic meaning and the consequent need for some way of narrowing down the range of possible interpretations of an utterance, as well as on the decontextualized character of written language.

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)0.010
metaresearch head score (Gemma)0.015
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 score0.010
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0080.073
Scholarly communication0.0080.014
Open science0.0030.004
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueWORDSame topicBiblical Studies and InterpretationFrench-language works237,207