Issues on the Translation of Certain English Collocations into Arabic: From Collocations to Free Constructions in the Target Language
Bibliographic record
Abstract
The translation of collocations between different languages is not always an easy task, but can at times be a problematic and a challenging practice amongst linguists and translators/interpreters. The present paper argues that the translation of English collocations into Arabic can be a flexible practice if Arabic possesses the equivalent collocation while the literal meaning of the whole English collocation is intended. The translator can still find an appropriate equivalent collocation in Arabic, even if the literal meaning of the first word in the English collocation is not intended. This, however, requires the translator to find a word in Arabic that conveys the intended meaning of the word in English and collocates with the other Arabic word simultaneously. The paper also claims that the translator may resort to make use of a free construction in Arabic to stand for the English collocation concerned. This often takes place if Arabic does not possess an equivalent collocation to the English collocation as the literal meaning of the latter is not the intended meaning, the verbs in the former and the latter differ in terms of type and function and/or the verb in the former can convey the intended meaning of the whole English collocation.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".