WORD STRESS MOSAIC OF GLOBAL ENGLISH: PLACEMENT AND PERCEPTION VARIANCE
Bibliographic record
Abstract
The study presents an overview of word stress variation in global English, arranged according to Kachruvian circles. The “inner circle” description is based on dictionaries and corpora data. The main distinctions between American English and British English in stress placement (2.5% of polysyllabic words) are also reflected in Canadian, Australian and New Zealand varieties, supplemented in each case by identifying national features (around 24% of the lexicon). American English is noted for an iambic pattern in disyllables of French origin. Canadian English demonstrated a slightly higher percentage of American patterns than British (41.7 vs. 36.1%) and a number of original Canadian ones (22.2%), predominantly with secondary stresses. In Australian data British patterns are more common than American (46.6% vs. 29.7%), while specific Australian ones account for 23.7%. New Zealand variety developed a greater number of secondary stresses, whereas Australian English ignored most of them. In the “outer circle” indigenized varieties of South-Asian region, like India and Singapore, are distinct from African varieties in Cameroon and Nigeria. In educated Indian English patterns common with the British standard account for 70% of stresses, while 30% bear the traces of the substrata, being particularly quantity-sensitive. Cameroonian and Nigerian Englishes shift stress either following the recessive tendency without exceptions or the “forward” one. Apart from differences in stress placement speakers of New Englishes are characterized by their choice of prominence cues. The “expanding circle” is still more diverse, as is manifested by Mandarin Chinese English with tone replacing stress, and “Russian Englishes”, defined as possessing a quantitative-qualitative stress. Implications for intercultural communication are suggested.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".