Bibliographic record
Abstract
An especially challenging aspect of transliteration is adopting an adequate system that can be used by an average person who may be unable to interpret diacritics or all the character and character combinations used in traditional and contemporary systems. Also, significant phonological differences between two languages such as those between Arabic and English, and the absence of a consistently used universal system, may result in numerous variations in the spelling of a name. This paper, part of a larger study that the authors are conducting, proposes a simplified system for the English transliteration of Oman‟s place names written in the Arabic script. The system is meant to be used on tourist maps, at tourist sites, on signposts, and in marketing and public relations material. Considering the importance of toponyms in conveying the historical and cultural heritage of a people, the extended project will examine the linguistic aspects of all Oman‟s toponyms, such as their morpho-syntactic and semantic properties and also their lexical-source domains. The research will further illustrate how cross-cultural influences on place name phonology may serve as narratives of identity and symbolic resistance to the dominant majority.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".