Bibliographic record
Abstract
The first section of this chapter introduces the changing fortunes of the Hebrew language, and the ways in which it came to be an object of study, before the emergence of formal Hebrew dictionaries. The first of these was written in 902 by Rav Saadia Gaon, and stands at the head of a tradition of medieval and Renaissance Jewish lexicography, in which both Biblical and post-Biblical Hebrew were addressed. Having described the principal dictionaries in this tradition, this chapter will comment on some of the ideas about the Hebrew language, and about lexicography, which they share. Hebrew lexicography is inextricably tied to the history of Hebrew itself. As is well known, Hebrew and its speakers suffered severe traumas throughout their history, traumas that did not allow transmission and continuous natural speech throughout the ages. The exile of the ten tribes to Assyria in 722 BC led to the loss of the Hebrew dialects of the northern Land of Israel, and the exile of the Kingdom of Judah to Babylon from 597 to 586 BC caused the loss of the natural living speech of the Judaic Hebrew state, the one represented basically in the books of the Bible as transmitted to us.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.059 |
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".