“Who Is My Neighbor?” Ethnic Boundaries and the Samaritan Other in Luke 10:25-37
Bibliographic record
Abstract
Abstract The story about the “Good Samaritan” in the gospel of Luke appears in the midst of a halakhic discussion between Jesus and a Judaean “lawyer” over who constitutes a “neighbor” (Luke 10:25-37). While scholars have often interpreted this pericope as a call for social inclusivity, the ways that Luke relies on and perpetuates prejudicial Judaean stereotypes about Samaritans have seldom been analyzed. This study draws on social-scientific and critical theory on ethnicity and the plethora of recent scholarship on Samaritan-Judaean interactions in order to explore the ways in which Luke’s text conveys prevalent ethnic stereotypes about Samaritans. It argues that Luke, like earlier and contemporaneous Judaean sources, appropriates an ethnographic representation of Samaritans as “proximate others” as part of a process of identity formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".