Bibliographic record
Abstract
The popularity of Dan Brown’s The Da Vinci Code has led to a surge of attacks on Christian Apocryphal literature by conservative New Testament scholars (e.g., Ben Witherington III, Craig Evans, Darrell L. Bock). The work of these scholars is transparently polemical—for example, Evans states that his book, Fabricating Jesus, was written “to defend the original witnesses to the life, death and resurrection of Jesus” (p. 17). And their methods are not new; indeed they use the same rhetorical strategies employed by such early heresiologists as Irenaeus, including the use of sarcasm and invective to describe their opponents, the intentional misrepresentation of the heretics’/scholars’ views and the content of the primary texts, the excerpting of material from the texts in order to expose their absurdities, and the demonization of their opponents by associating them with the powers of darkness. This article illustrates the parallels between modern critics and the ancient heresy hunters but focuses particularly on how the two groups use and abuse the apocryphal texts. Perhaps we can learn from the contemporary debate something about the reception of the Christian Apocrypha in antiquity.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".