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A Disabled Apostle

2023· book· en· W4386120502 on OpenAlexaff
Isaac T. Soon

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsCrandall University
Fundersnot available
KeywordsApostleContext (archaeology)ScholarshipAbleismHistoryEarly ChristianityEpigraphyDisability studiesNew TestamentLiteratureSociologyAestheticsClassicsGender studiesArtPhilosophyLawPolitical scienceTheologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Speculation around the health of Paul the apostle has been present since soon after his death. Recently scholars have understood Paul to be disabled but have been wary of isolating precisely what his disabilities may have been or whether they are important for understanding his writings. This book is the first full-length study of Paul the apostle and disability. Using insights from contemporary disability studies, Isaac Soon analyses features of Paul’s body in his ancient Mediterranean context to understand the ways in which his body was disabled. Focusing on three such ancient disabilities—demonization, circumcision, and short stature—this book draws on a rich variety of ancient evidence, from textual sources and epigraphy to ancient visual culture, to analyse ancient bodily ideals and the negative cultural effects such ‘deviant’ persons generated. The book also examines Paul’s use of his own disabilities in his letters and shows how disability is not subsidiary to his thought but a central aspect of it. This book also provides scholars with a new method for uncovering previously unrecognized disabilities in the ancient world. Last of all, it critiques the latent ableism in much New Testament scholarship, which assumes that the figures of the early Jesus movement were able-bodied.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.009
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.058
GPT teacher head0.249
Teacher spread0.191 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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