Bibliographic record
Abstract
Written for a general audience, the essays collected here present refreshing and often humorous glimpses of various topics in Jewish history and traditional religious literature. Inspired by the diversity of Jewish thought, author and scholar Eliezer Segal sheds light on the social and political forces that have brought the Jewish community together in the past and still speak with familiarity to a modern western culture. Enlightening and entertaining, Professor Segal's writing is a rare blend of scholarship and wit, highlighting contemporary experiences that bring the rich heritage of Jewish civilization to life for the everyday reader. With an extensive and broad knowledge of ancient and medieval Jewish social and religious traditions, Segal deftly crafts anecdotes and explanations that address the tribulations of contemporary life. From topics as diverse as panhandling, tennis, vampires, and the history of the tomato to themes as universal as weddings, charity, and taxation, the essays presented here, some for the first time in English, all include detailed notes on sources for further reading. Equally suited to those after a light-hearted romp or those on a serious quest for knowledge, Ask Now of the Days that Are Past is sure to satisfy anyone who has ever wondered how the past still influences us today.
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 | 0.000 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.054 |
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".