Bibliographic record
Abstract
The study of the unprecedented revival of Hebrew in (pre-Israel) Palestine (approx. 1890–1914) has focused on the status of the language, because the revival has been rightly viewed as resulting from status planning. However, corpus planning, or codification, also served as a critical component of the Revival. Though Hebrew had been used for almost two millennia in written form, mainly as a language of religion, codification was needed in several areas — selection and harmonization of pronunciation, unification of spelling, etc. Still, the greatest task was adapting the language lexically to the modern world. Codification went on in Hebrew, in fact, for over a millennium by generations of writers and translators of various types of texts, culminating in the formation of a modern literature, probably the most instrumental factor enabling the Revival. Lexicalization in the Revival itself was partly done by the Hebrew Language Committee, but mostly by individuals. Ben-Yehuda drew words from old texts and created his own as a scholarly activity and to meet his lexical needs as a newspaper publisher and the first Hebrew dictionary compiler. Others included the writer and journalist Ben-Avi and the national poet Bialik, who drew words from earlier texts or created their own only when they needed them. Other individuals coined countless words to meet their communication needs — writers, journalists, educators, translators, publishers, editors, and language-conscious political leaders. Apart from drawing words from old texts with their original or new meanings, methods included: coining new words from old roots; using old, dormant words as different parts of speech; reducing expressions into single words; borrowing; loan translation; popular etymology; adding prefixes, suffixes or infixes to existing words; and merging pairs of words into single ones.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".