The Myth of Reference Varieties in English Pronunciation across the Subcontinent, Egypt and Kingdom of Saudi Arabia
Bibliographic record
Abstract
The present study aims at exploring the differences in pronunciation more or less prevailing in the Indian Subcontinent and Arab world with a special focus on Pakistan, India, Bangladesh, Saudi Arabia, and Egypt. The study identifies the most common factors that affect English pronunciation in general: (i) some phonemic differences that exist in L1 and English as a target language, (ii) Improper teaching and learning of English pronunciation. When non-native speakers of English exchange their ideas among themselves, their comprehension is to the maximum. But their pronunciation seems problematic in case the speaker or interlocutor is a native speaker. A test for the nationals of the lands included in the study was developed and administered to identify and specify the exact area(s) of pronunciation difficulties either consonants or vowels at segmental level of phonology. The analysis and conclusion of the test fully proved that English pronunciation is deeply influenced by the sound system of indigenous languages. As a matter of fact, English pronunciation of some non-native speakers, through their best possible efforts, may be closer to native speakers but not exactly like that of natives. The fact is that native and non-native differences in English pronunciation are unquestionable. Moreover, non-natives living in different areas (sometimes of the same community) also differ more or less in their pronunciation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".