Awareness of L2 American English Word Stress: Implications for Teaching Speakers of Indo-Aryan Languages
Bibliographic record
Abstract
This study aims to investigate the word stress placement in English and Sindhi words in learners from Indo-Aryan language and American English backgrounds. Since correct placement of word stress is key for L2 English intelligibility, and it is known that native language background affects English language learners’ word stress perception and production. The study explores English language learners’ intuition through behavioral data from the native speakers of Sindhi and American native speakers to compare their awareness of word stress in L1 and L2. It further investigates learner’s stress patterns by measuring their reports of word stress location in their Sindhi and in their L2 English. There were twenty native speakers (10 from Sindh, Pakistan-10 from Illinois State, America) who were recruited from the location in their countries. Results of three experiments show that Sindhi native speakers have less awareness of stress location in their native language than native English controls, and this effect carries into their L2 English. Teachers of Sindhi-speaking students should be prepared to provide explicit training on word stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".