Common Problems in Translation of Political Texts: The Case of English and Kurdish Languages
Bibliographic record
Abstract
This study investigated and examined the problems pertaining to the translation of political texts.It intends to deal with the main translation difficulties and problems emerging in the process of translation of political texts from English to Kurdish Language.Concerning theoretical background, the study followed the Nord's classification of translation problems (1997): pragmatic, cultural, linguistic and text-specific issues, then the study reviews the relevant literature to address certain issues such as the definitions of translation, approaches to translation, specialized translation, translation problems, features of political language, and translation and political language.This is followed by a brief presentation of the methodology of this research.The study concludes that there are no specific methods in translating political texts due to the fact that translation is not only a practical process that employs translation techniques, but also an issue of comprehending cultural, historical and political situations and overtones.
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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 | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".