Doing Torah, Imitating Yahweh: A Reconsideration of the Good Samaritan Story
Bibliographic record
Abstract
Scholars often puzzle over why the discussion about Torah obedience in Luke 10:25–29 does not appear to fit coherently with the story of the Good Samaritan that follows it (10:30–37). Was this an oversight on Luke’s part, a lack of editorial finesse, or did he have other aims? In this paper, I will argue that the apparent shift in logic, marked by the transformation of the lawyer’s question from ‘Who is my neighbour?’ (10:29) to ‘Who acts as a neighbour?’ (10:36), invites the lawyer to realign his interpretation of Lev 19:18 with the theology of imitatio Dei already present in Leviticus 19: The one who properly fulfills Lev 19:18 does so by imitating Yahweh. Within the context of Luke’s Gospel, moreover, Luke 10:25–37 illustrates how Luke both affirms and expands the terms of Torah obedience such that proper fulfilment of the Mosaic Law requires a disclosure of and participation in the very nature of God.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".