Bibliographic record
Abstract
Dov Noy was my teacher, but not mine alone. He introduced folklore into Jewish Studies, and Jewish folklore into the discipline of folklore. Stith Thompson (1885-1976) integrated Dov Noy's dissertation (as Dov Neuman) "Motif-Index of Talmudic-Midrashic Literature" (1954) into the second edition of the Motif-Index of Folk-Literature and established its subject, and Dov Noy himself, firmly in the international community of folklore scholars. Upon the completion of his studies at Indiana University, Noy joined the faculty of the Hebrew University in Jerusalem in 1955 and began offering courses in folklore in the Hebrew Literature and the Yiddish departments. He was an inspirational teacher who attracted students and motivated them to continue the systematic research and teaching of Jewish folklore, and they have done so at the Hebrew University and in other Israeli universities. He himself taught Jewish folklore in American and Canadian universities, and inspired scholars, writers, and storytellers to explore and revive the art of storytelling in Jewish societies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.243 | 0.136 |
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".