Bibliographic record
Abstract
Because Axis Mundi has, to date, dedicated itself solely to publishing articles and not review essays, the publication of the following papers marks Axis Mundi’s review publishing “debut.” This “review symposium” on Burton L. Mack’s A Myth of Innocence is therefore something of a milestone for the journal. It is also an exciting occasion for scholars such as myself, whose research interests primarily revolve around the field of Christian origins, because the publication of this book in 1988 was a milestone in the establishment of our field. In a time when most students of early Christianity were occupied with hermeneutical studies of the New Testament, trying to decipher the texts and make them relevant for contemporary theological insight, Mack was a pioneer, treating the texts not as material for exegesis but as “artifacts” from prior communities engaged in social formation and rhetorical representation. Mack viewed these textual representations as “identity markers” by which groups of early Christians defined themselves in the hectic social economies of the Hellenistic world. In this way, he helped establish a distinctly North American style of biblical studies, which many now designate as the study of "Christian origins,” in contradistinction to the more traditional field of “New Testament studies.”
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 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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".