Can Syllabicating Transliterated Arabic Road Signs Improve their Pronunciation by Non-Native Speakers of Arabic?
Bibliographic record
Abstract
The present study investigated the effect of syllabicating transliterated Arabic road signs on their pronunciation by non-native speakers of Arabic. It was hypothesized that syllabicating transliterated road signs results in better pronunciation by non-native speakers of Arabic than writing them as whole words. Twenty subjects who participated voluntarily in the study were asked to read a list of twenty Arabic road signs transliterated into English, taken from real road signs in the UAE and written as whole words (Method 1). The same signs were syllabicated and shuffled before they were given to the subjects to read them (Method 2). Participants were recorded in both times. Records were played by three raters who scored participants on a Likert scale of four categories. For reliability of agreement, Fleiss' Kappa revealed substantial agreement (K= 0.667) for the Method 1 and moderate agreement (K= 0.599) for Method 2. Paired T test was applied to test the difference between the means of the first and the second method. The results showed a significant difference between the two means (t = -11.145, p<= 0.0001), which supports the research hypothesis. Results were discussed and implications were provided with further research suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".