Empathy, Suspicion, and Reading the Hebrew Bible: Comparative Methods in Studying and Teaching the History of Religion
Bibliographic record
Abstract
ABSTRACT Many in today's society recognize the ability to empathize with others as an important personal virtue. In the context of higher education, extending empathy to students is regarded as a strategy that educators can employ to retain students and help them to succeed in their classes. Beyond its utility as a virtue and its role in student success, though, empathy is also an important tool for scholars engaged in the comparative study of religions. I argue that educators who practice and teach empathy to their students better prepare them to empathize with practitioners of ancient religions as humans. When we view these practitioners as humans, we are much better equipped to understand the worldviews that underlie their motivations, making us better scholars of religion and better historians.
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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 | 0.059 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".