Jordanian Folkloric Songs in Translation: Mousa’s Song They Have Passed by Without a Company as a Case Study
Bibliographic record
Abstract
Folkloric song-translation is a research area that diverges acutely from the centre of interest of interlingual-intercultural transfer in general, and Arabic-English translation studies in particular. This paper attempts to shed light into Abdu Mousa’s culture-bound songmarren wa ma ma’hin hada(they have passed by without a company), as an instance of the challenges that folkloric songs may pose in translation, and how, when translating between cultures with different discursive properties, the translator has a certainleewaywhen reformulating the lingual-cultural import of the source text for target readers. Drawing on Low’s (2005a)Pentathlon Approach, and placing a strong emphasis oncontent, the study highlights the problems and difficulties involved in translating this type of song, and demonstrates a number of unique aspects of translating folkloric songs, which involve elements of sense, naturalness, cultural references, and how these elements are interconnected and entangled with each other. These insurmountable difficulties are accounted for by the existing sharp linguistic and cultural differences between Arabic and English, and, the incompatibilities between the two workingconcept systemsof the two languages, which add to the intricacy of translating this type of literature. On a less formal level, colloquialism has been found to have had its way to the source language text, a factor which further complicates the abridgement process.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".