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Record W6887788378 · doi:10.17613/m6kb7h

Fakes, Forgeries, and Fictions: Writing Ancient and Modern Christian Apocrypha. (Introduction and Table of Contents).

2017· article· en· W6887788378 on OpenAlexaboutno aff

Bibliographic record

VenueHumanities Commons CORE (Modern Language Association / Columbia University) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical, Religious, and Philosophical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsApocryphaGospelNew TestamentPapyrusSAINTExegesisOld TestamentTable (database)

Abstract

fetched live from OpenAlex

Fakes, Forgeries, and Fictions examines the possible motivations behind the production of apocryphal Christian texts. Did the authors of Christian apocrypha intend to deceive others about the true origins of their writings? Did they do so in a way that is distinctly different from New Testament scriptural writings? What would phrases like "intended to deceive" or "true origins" even mean in various historical and cultural contexts? The papers in this volume, presented in September 2015 at York University in Toronto, discuss texts from as early as second-century papyrus fragments to modern apocrypha, such as tales of Jesus in India in the nineteenth-century Life of Saint Issa. The highlights of the collection include a keynote address by Bart Ehrman ("Apocryphal Forgeries: The Logic of Literary Deceit") and a panel discussion on the Gospel of Jesus' Wife reflecting on what reactions to this particular text—primarily on biblioblogs—can tell us about the creation, transmission, and reception of apocryphal Christian literature. The eye-opening papers presented at the panel caution and enlighten readers about the ethics of studying unprovenanced texts, the challenges facing female scholars both in the academy and online, and the shifting dynamics between online and traditional print scholarship.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.204
Teacher spread0.170 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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