Critique of Aspects of Translation of the Poetry of Pre-Islamic Poets and also of “Wormhoudt’s” Translation of al-Mutanabbi
Bibliographic record
Abstract
Errors arise in the translation of poetic literature as a result of gaps in the translator's knowledge of the historical, social, and cultural context in which the poem was written. However, it is not enough for the translator to have knowledge of this sort, he must also know about the kind of idiomatic expressions in vogue which the poet tends to use and the contemporary allusions intended in his choice of words, metaphors..., etc. A further source of error stems from a misunderstanding of grammatical, or stylistic features of the poetry. All poets manipulate the grammar of their language in their own characteristic way. Unless the translator is well acquainted with these features, he may not faithfully represent their semantic effect in the target language. This paper deals with some cultural and linguistic aspects of some translations of the pre-Islamic poets and also of "Wormhoudt's" translation of al-"Mutanabbi" with examples of errors arising from cultural and linguistic misunderstanding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".