Experience Effects on the Development of Late Second Language Learners’ Oral Proficiency
Bibliographic record
Abstract
The aim of this study was to evaluate the effects of second language (L2) experience–operationalized as length of residence (LOR) in Canada—on late Japanese learners of English. Data collected from 65 participants consisted of three groups of learners (short‐, mid‐, and long‐LOR groups) and two baseline groups of native Japanese and native English speakers, with 13 participants in each group. The global quality of the participants’ spontaneous speech production was initially judged by 10 native‐speaking English raters for accentedness (linguistic nativelikeness) and comprehensibility (ease of understanding) and then submitted to segmental, prosodic, temporal, lexical, and grammatical analyses. According to the results, LOR was generally predictive of improved comprehensibility through its association with adequate and varied prosody, optimal speech rate, and proper lexicogrammar usage. In contrast, contributions of LOR to accentedness remained unclear, with less accented speech linked to refined segmental accuracy, vocabulary richness, and grammatical complexity. These findings suggest that learners continue to improve in their L2 oral proficiency over an extensive period of L2 immersion (e.g., 6 years of LOR), and they likely do so by paying selective attention to certain linguistic domains closely linked to comprehensibility—but not necessarily relevant to accentedness—for the purpose of successful L2 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".