Prorok w dawnych i współczesnych translacjach Biblii i Koranu
Bibliographic record
Abstract
The article attempts to analyze different ways of translating words referring to “a prophet” from Arabic into Slavonic languages in Tatar writings of the Grand Duchy of Lithuania (GDL) and Polish translations of the Koran in comparison to prophet designations present in the Polish translations of the Bible. It is also an attempt to find out whether collocations with the word prophet present in the GDL Tatar writings and Polish translations of the Koran are characteristic of this type of texts when the so called Koran phraseology is created, and whether and to what extent they reflect biblical phraseology or general Polish lexis. If they do reflect this, the scope and nature of the relations between biblical and Koranic translations will be determined.Moreover, the image of a prophet emerging from biblical and Koranic translations is presented. The source material are texts which vary with regard to formality and time since the source of the vocabulary excerption are both Polish translations of the Koran and the GDL Tatar historic works written in Arabic script that require transcription and transliteration, as well as Polish translations of the Bible from the 16th and 17th centuries and contemporary ones.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".