Memory, Tradition and Text: Uses of the Past in Early Christianity - Edited By Alan Kirk and Tom Thatcher [Review of the book <em>Memory, Tradition and Text: Uses of the Past in Early Christianity</em>, by A. Kirk & T. Thatcher, Ed.]
Bibliographic record
Abstract
The aim of this collection of essays is, at least in part, to remedy the lack of attention that studies of early Christianity have paid to recent developments, in the fields of sociology and anthropology, in the study of memory. An excellent introductory survey by Alan Kirk of recent developments in memory studies is followed by eleven essays applying some aspect of the approach to various texts or problems in the study of early Christianity, and then by responses by Werner Kelber and Barry Schwartz. While the various contributions interact in different ways with the relevant theories and models, all share an understanding of memory as a complex interaction between knowledge of the past and its appropriation in the present. Although the collection as a whole is strong, a few essays stand out: Richard Horsley’s “Prominent Patterns in the Social Memory of Jesus and Friends,” in which he locates possible continuity between Jesus and later literary traditions such as Q and Mark in general patterns of Israelite social memory; and Phillip Esler’s reading of the Israelite heroes presented in Heb 11. The insights generated by the application of memory studies to the study of early Christianity are welcome, and, as the editors suggest, long overdue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".