A conceptual model of second language pronunciation in communicative contexts: Implications for children’s bilingual education
Bibliographic record
Abstract
Second language (L2) pronunciation patterns that differ from those of first language (L1) speakers can affect communication effectiveness. Research on children's L2 pronunciation in bilingual education that involves non-English languages is much needed for the field of language acquisition. Due to limited research in these specific populations and languages, researchers often need to refer to literature on L2 pronunciation in general. However, the multidisciplinary literature can be difficult to access. This paper draws on research from different disciplines to provide a brief but holistic overview of L2 pronunciation. A conceptual model of L2 pronunciation is developed to organize multidisciplinary literature, including interlocutors' interactions at three layers: the sociopsychological, acquisitional, and productive-perceptual layers. Narrative literature review method is used to identify themes and gaps in the field. It is suggested that challenges related to L2 pronunciation exist in communication. However, the interlocutors share communication responsibilities and can improve their communicative and cultural competencies. Research gaps are identified and indicate that more studies on child populations and non-English L2s are warranted to advance the field. Furthermore, we advocate for evidence-based education and training programs to improve linguistic and cultural competencies for both L1 speakers and L2 speakers to facilitate intercultural communication.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".