Transliteration of Arabic Words/Phrase into English: An Exploration of Ambiguity Markers
Bibliographic record
Abstract
Transliteration is a useful process when communication involves a language pair out of which each follows a different script, such as Arabic and English. One danger posed by this process is the ambiguity between the source and target of the communication. This study analyzes the intricate process of Arabic to English transliteration and the factor that make it ambiguous. The study aims to identify, categorize, and analyze the ambiguity markers that frequently arise during the transliteration of Arabic script into the Latin alphabet. The study interviewed 6 specialist translators in the Saudi context to identify the source of difficulties they encounter and which may bring ambiguities to the readers at various language levels. Results indicated that non-standardization in translation from Arabic to English was a cause of ambiguity in transliteration. Finally, regional dialects could not be adjusted in the transliteration spectrum in Google Translate. This research contributes to the field of transliteration studies by providing a comprehensive framework for understanding and addressing ambiguity in the transliteration 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".