Bibliographic record
Abstract
En aquest article, l'autor analitza la tasca de codificacio lexica duta a terme en el periode de de l'hebreu modern. Es pot considerar que la de l'hebreu modern compren tres periodes, en cada un dels quals s'ha intentat aconseguir com a minim un objectiu de planificacio linguistica. El primer d'aquests periodes es el de la recuperacio linguistica (1890-1914), en el qual va tenir lloc la d'aquesta llengua a Palestina. Al comencament d'aquesta recuperacio, el lexic hebreu era tan inadequat per a la vida moderna ?ja que hi mancaven paraules per a conceptes com tomaquet, serios i diari? que alguns dirigents van posar en dubte la capacitat de de la llengua. Per tant, calia dur a terme una planificacio del corpus per a emplenar aquest gran buit lexic. Aquest aspecte de la es va assolir gracies als esforcos conjunts d'educadors, escriptors, traductors, etc., com tambe d'innombrables individus amb consciencia linguistica. Es va dur a terme de diverses maneres: recuperant paraules i arrels antigues, creant noves paraules a partir d'arrels i termes antics, combinant paraules existents, completant models amb complements d'arrel, amb prestecs de paraules i arrels, etc. Tota aquesta campanya laboriosa i aparentment interminable va donar resultat, i actualment l'hebreu es una llengua moderna, estandarditzada i normalitzada en tots els aspectes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".