Can a Segulah free an Agunah? : Jewish beliefs and practices for locating a drowned body
Bibliographic record
Abstract
Can A Segulah Free an Agunah? Jewish Beliefs and Practices for Locating a Drowned Body By Bency EichornBency Eichorn learns in kollel and, on the side, has been researching about various segulos.For his wedding he authored a book, Simchas Zion, discussing the segulah of keeping the afikomom from year-to-year.The post below is a small part of a much larger project on this segulah and has been adapted for the blog.In light of the recent drowning of Los Angeles's Naftoli Smolyansky A"H, much discussion has ensued about the segulah performed to recover his body.This same segulah, which involves floating a loaf of bread and candle in the water to locate the missing corpse, last year when Toronto Rabbonim considered performing it in order to locate the missing body of Eli Horowitz A"H, who had drowned the previous year.There is much skeptism regarding this segulah, some consider it witchcraft and claim that it has no basis in Judaism, deriving instead from non-Jewish sources.In this article, I will outline the development of similar segulot used throughout the ages and discuss how these methods were practiced by Jews and non-Jews alike.As my research on this topic is ongoing, I do not attempt to draw conclusions, but rather I hope to draw attention to primary and little-noted sources for these segulot.In effect, this will indicate how wide-spread these segulot were, specifically among Jews.This will suggest that their origins extend further than the tale recounted in Twain's Hucklebery Finn and can be traced to early Jewish sources.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".