Bibliographic record
Abstract
ABSTRACT What are best practices for teaching the Pentateuch and the Hebrew Bible more broadly? How can we introduce students to ways of reading biblical texts that are eye‐opening, empowering, and accessible? In this paper, I explore some of the challenges and opportunities that we face as biblical studies professors, and I profile a handful of examples that have been especially fruitful in my experience teaching undergraduates. On the whole, my approach is text‐centered and student‐centered. Regarding the former, rather than promoting a one‐size‐fits‐all method, I try to approach each text or set of texts on its/their own terms, asking which method, set of questions, or article will best illuminate it. Regarding the latter, I advocate for pedagogical practices that encourage students to reach conclusions on their own, especially when it comes to complex questions pertaining to the development of texts over time. Finally, I believe that we must make our syllabi, exams, and research more diverse and inclusive if we want the field to thrive and if we want more students to have “a place at the table” in the years to come.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".