What branches grow out of this stony rubbish? Christian Origins and the Study of Religion
Bibliographic record
Abstract
The following paper argues for the potential relevance of scholarship on New Testament/Christian origins to the study of religion generally, in response to recent institutional developments that have driven the Society of Biblical Literature and American Academy of Religion to hold separate meetings. The paper claims that Christian Origins scholarship suggests a series of cautions — about originary stories, the boundaries of traditions, and the predictability of historical developments — as well as some substantive contributions — regarding the desultory character of ‘‘religious’’ interventions, the role of narrative, and the input of intellectuals — to our views of religion that should be of interest to students of other data-sets. At the same time, and in some ways more to the point, it is incumbent upon those scholars of Christian Origins who aim to situate their scholarship within the larger field of the study of religion to be willing to generalize and not only to draw broad conclusions about the development and origins of ancient Christianity, but also to ensure that their own analyses and conclusions are, at least potentially, formulatable in terms of expansive generalizations about human behavior. Thus not only does the incorporation of Christian origins into the study of religion potentially add at least to the relevant data-set of the latter, but it may also be a way to enhance the responsibility and intelligibility of the former.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".