The influence of L1 on the acquisition of stress and intonation patterns of English: a case in the United States
Bibliographic record
Abstract
Most ESL learners have a foreign accent in their speech owing to the significant influence of their mother tongue on their production of English pronunciation. Most previous literature has stressed the segmental area that focuses on single sounds and provided an accurate list of the tricky vowels, along with the level of difficulty to be avoided. This research aimed to measure the level of awareness of teachers and learners regarding the topic, both qualitatively and quantitatively, and presented some efficient methods for presenting intonation, stress, and rhythm patterns. Additionally, the study examined the role of the curriculum in guiding the learning process and the impact of exposing learners to native speakers in defining certain variables. To collect the data, the researcher used interviews, observation, and questionnaires. There were 15 non-native and TESL Ontario-licensed teachers participating in this study, who were interviewed. The study found a significant link between the teachers’ degrees and experience and the level of awareness, tolerance to the learners’ mistakes in this area, and the curriculum focus. This study contributes to the understanding that exposing ESL learners to native speakers’ accents can help facilitate the concept of acculturation and prevent diglossia in the English language learning process.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".