Ethnicity and Inclusion: Religion, Race, and Whiteness in Construction of Jewish and Christian Identities, by David G. Horrell
Bibliographic record
Abstract
David G. Horrell's recent book, Ethnicity, and Inclusion: Religion, Race, and Whiteness in Constructions of Jewish and Christian Identities, goes deep into the heart of New Testament studies to examine how this line of inquiry has shaped Jewish and Christian identities in historical narratives.Horrell argues that, despite significant developments in this field and periodic paradigm shifts in scholarly thinking, the idea of a structural dichotomy between Jewish and Christian identities still prevails, a dichotomy which insists on Jewish particularism and Christian universalism.Horrell's objective is to understand the origin of this assumed dichotomy and then to demonstrate to what extent scholarly perceptions about Jewish and Christian identities have changed over time.He approaches this research in three ways: by surveying New Testament scholarship from the late nineteenth century to the present, by analyzing ancient sources to compare certain practical and ideological aspects of Christianity and Judaism, and finally, by reflecting on his findings in light of sociopolitical changes and epistemological factors.The nine
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".