BACK TO ZECHARIAH FRANKEL AND LOUIS JACOBS? ON INTEGRATING ACADEMIC TALMUDIC SCHOLARSHIP INTO ISRAELI RELIGIOUS ZIONIST YESHIVAS AND THE SPECTRE OF THE HISTORICAL DEVELOPMENT OF THE HALAKHAH
Bibliographic record
Abstract
This paper will discuss three new methods of teaching Talmud that Israeli Religious Zionist Yeshivas have adopted over the past two decades against the backdrop of the hitherto and perhaps still dominant approach to teaching Talmud in these Yeshivas, namely, the classical conceptual, ahistorical, highly abstract “Brisker” approach: (1) a modified Brisker approach; (2) the “Torat Eretz Yisrael,” “the Torah of the Land of Israel” approach; and (3) what I would call the “shiluv” approach, a term that implies forming a new and harmonious whole. What these three approaches have in common is the desire to retain the conceptual analysis of the Brisker approach, but to abandon its strict formalism and combine it with the search for religious meaning and significance. However, while the first two approaches in their search for the religious significance of the text generally eschew the use of the critical methodologies employed by academic Talmudic scholarship, the third approach embraces the use of those methodologies and seeks to integrate them into the world of traditional Talmud study. I will focus on the theological challenges raised by this attempted integration and on how the exponents of the “shiluv” approach have sought to deal with them.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".