The Nexus between Textual Criticism and Linguistics: A Case Study from Leviticus
Bibliographic record
Abstract
Forty-five years after James Barr’s Comparative Philology and the Text of the Old Testament appeared, it is time to reiterate his call for a balanced approach to philology and textual criticism. Though the essential issues are the same as when Barr wrote, the amount of textual data from the Dead Sea Scrolls as well as methodological challenges to the standard view of the linguistic history of ancient Hebrew have produced a significantly more complex situation. As scholars move forward in both subdisciplines of Hebrew studies—textual criticism and historical linguistics—it is more critical than ever to keep in mind that the history of the text and the history of the language are inextricably bound to each other. Using two variants in Leviticus, I will illustrate what a reasonably balanced approach looks like from the perspective of a Hebrew linguist, with the hope that textual critics and Hebrew linguists will see the need to work more closely with each other.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.038 | 0.024 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".