Absence of English Phonemes from Arabic; The Impact on EFL and ESL learners’ Production of Loanwords
Bibliographic record
Abstract
The present study aims to address whether the absence of foreign English phonemes from the Arabic phonemic inventory have impacts of EFL and ESL learners’ pronunciation of English loanwords differently. The study adopts a comparative approach, seeking to examine whether the two groups of learners used the same or different phonemes for substituting or approximating the target phonemes. 28 English loanwords were utilized to test the productions of 15 learners at Salman Bin Abdul-Aziz University, Saudi Arabia (EFL) and 15 learners at the Center for English as a Second Language in Southern Illinois University, USA (ESL). Probing the impact of the learners’ L1, Arabic language, on the production of loanwords via numerous theories and frameworks such as transfer, approximation, and the Markedness Differential Hypothesis showed that these English loanwords underwent certain phonological modifications. Both EFL and ESL learners showed transfer from L1 to L2, native Arabic phonological processes, while only ESL learners showed a universal pattern, such as VOT approximation. That is, both EFL and ESL learners substituted /v, ɹ, tʃ/ with /f, r, ʃ/, but they differed in their production of /p/. While EFL learners substituted /p/ with /b/, ESL learners reflected approximated sound to /p/.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".