Sprachethik im Neuen Testament: Eine Analyse Des Frühchristlichen Diskurses im Matthäusevangelium, Im Jakobusbrief Und im 1. Petrusbrief By SusanneLuther. Wissenschaftliche Untersuchungen zum Neuen Testament II, 394. Tübingen: Mohr Siebeck, 2015. Pp. xii + 572. Paper, Є99.00.
Bibliographic record
Abstract
This book is based on a dissertation completed in 2012 in the Faculty of Theology at Friedrich-Alexander-Universität Erlangen-Nürnberg under the direction of Oda Wischmeyer. Luther focuses on the speech ethics of three New Testament texts: the Gospel of Matthew, the Letter of James, and the Letter of 1 Peter. In the first chapter, Luther defines her terms and explains her methodology, which is that of discourse analysis. In subsequent chapters, she explores a variety of topoi related to speech ethics in antiquity. These include what the texts say about anger, the control of the tongue, false and inadequate speech, swearing or oath-taking and being truthful, the integrity of the speaker, as well as the context for uttering rebukes. The analysis is conducted in light of and in comparison with Greco-Roman and Jewish practices. Luther concludes that these particular New Testament writings are quite consistent when it comes to speech ethics. She also includes an appendix in which she discusses the “Law” in James. This is a substantial, interesting, and well researched study. It will be important for scholars working on speech ethics in antiquity, as well as those who focus on James in particular.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".