How long can naturalistic L2 pronunciation learning continue in adults? A 10-year study
Bibliographic record
Abstract
We examined the naturalistic pronunciation development of two groups of L2 speakers over 10 years. Initially, 50 beginner ESL students participated in production tasks; despite attrition, the tasks were administered eight more times. Here we report listener judgements of accentedness, comprehensibility and fluency for the remaining six Mandarin and 12 Slavic language speakers at Year 10. Analyses of listener judgments of accentedness, comprehensibility, and fluency of utterances recorded at the 2-month, 1-year, 2-year, 7-year and 10-year points revealed that the Slavic language speakers improved in comprehensibility and fluency at each comparison point, while the Mandarin speakers’ results were variable; there was improvement in comprehensibility from Year 7 to Year 10, but only after worsening at earlier points. The Slavic language group showed improvement in accentedness several times, whereas the Mandarin group showed no improvement in accentedness at any point. The data were examined for individual differences in learning trajectories. Interview responses and a survey of language use were compared to participants’ trajectories. Some speakers showed steady improvement from Year 7 to Year 10, but the majority plateaued or regressed. We also elicited speakers’ views of their progress. The results are interpreted through Complexity Theory and the Willingness to Communicate framework. Suggestions are made for research and teaching interventions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".