Fakes, Forgeries, and Fictions: Writing Ancient and Modern Christian Apocrypha. (Introduction and Table of Contents).
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".